Exploring the drivers of price variation in orthopaedic radical bone tumor resection: A nationwide database study

Devika A Shenoy; William C Cruz; Shamik Bhat; Katelyn Parsons; Aaron D Therien; Kevin A Wu; Christian A Pean; William C Eward

doi:10.1371/journal.pone.0343676

. 2026 Feb 26;21(2):e0343676. doi: 10.1371/journal.pone.0343676

Exploring the drivers of price variation in orthopaedic radical bone tumor resection: A nationwide database study

Devika A Shenoy ¹, William C Cruz ², Shamik Bhat ^3,^4,^*, Katelyn Parsons ¹, Aaron D Therien ¹, Kevin A Wu ⁵, Christian A Pean ¹, William C Eward ¹

Editor: Xiaoen Wei⁶

PMCID: PMC12944713 PMID: 41746972

Abstract

Background

Radical resection of bone tumors is a clinically effective but costly procedure. Despite the implementation of federal price transparency mandates, little is known about the nationwide variation in negotiated prices for these specialized oncologic surgeries. This study aimed to quantify the variation in negotiated rates for radical resection of the humerus and femur/knee and identify associated hospital, payor, and state-policy drivers.

Methods

This cross-sectional study analyzed hospital-negotiated payor rates from the Turquoise Health database for current procedural terminology (CPT) codes 24150 (humerus resection) and 27365 (femur/knee resection). Multivariate linear regression was used to determine the associations between hospital size and type, payor class, and state-level policies (Medicaid expansion, Certificate of Need [CoN] laws, All-Payer Claims Database [APCD] mandates, and Nurse Practitioner [NP] scope of practice) on negotiated payor rates.

Results

A total of 285,857 negotiated rates were analyzed. Significant price variation was observed across all factors. Large hospitals (>1000 beds) and Critical Access Hospitals (for femur/knee resection only) had significantly higher rates. CoN laws were associated with higher prices for both procedures (+$348.25 and +$667.98, respectively), as were APCD mandates for femur/knee resections (+$1231.24). Medicare Advantage plans paid inconsistently compared to commercial plans, paying more for humerus but substantially less for femur/knee resections.

Discussion

Negotiated prices for radical bone tumor resection are highly variable and influenced by a complex interplay of market dynamics, challenging the assumption that price transparency alone can standardize healthcare costs for specialized care.

Introduction

The management of primary and metastatic bone tumors has evolved greatly over the past few decades, with new options for advanced surgical reconstructions and targeted immunotherapy providing patients with multifaceted treatment options. [1–4] While primary bone cancers account for only 0.2% of primary malignancies in the United States (U.S.), approximately 5.1% of patients with other malignancies report developing bone metastases. [5,6] The national cost burden for patients with metastatic bone disease estimated at $12.6 billion dollars in 2007. [7] Despite advancements in medical therapies [8], surgical management remains at the cornerstone of ensuring local tumor control, and preserving access to high-quality surgical care is essential for patients. [9] For example, radical resection of bone tumors involves removing sections from long bones, often in the setting of primary bone tumors such as osteosarcoma, or for metastatic bone disease. [10–12] While clinically effective in providing local tumor control and preserving limb function [10–12], there is limited knowledge regarding the costs of these procedures in the past decade.

Due to their impacts on both direct patient costs and costs to the healthcare system, the financial dynamics of major surgical procedures are increasingly under scrutiny. [13–18] The recent implementation of the Centers for Medicare & Medicaid Services (CMS) Price Transparency Rule provides an opportunity to address these concerns. [19] By mandating that hospitals publicly disclose their privately negotiated rates with insurers, this policy has offered the ability to investigate the true, market-driven prices for specific procedures on a national scale, making it possible to explore the financial landscape of specialized oncologic surgeries. Recent studies leveraging price transparency data have uncovered significant, often unexplained, variation in the negotiated prices for common procedures in both general orthopaedics and surgical oncology. [14,15,17,18,20] For example, one cross-sectional study of 15,013 Medicare beneficiaries found large, regional variations in negotiated payor rates for common oncologic operations, as well as mild associations between high rates and adverse clinical outcomes. [18] Additionally, another study found that healthcare policies, such as Medicaid Expansion and Certificate of Need (CoN) status, were associated in large variations in payor rates for total hip arthroplasty. [14]

However, it is unknown if similar price variability exists for high-stakes, less common procedures like the radical resection of bone tumors. Therefore, the present study leverages a national price transparency database to analyze two analogous but distinct procedures, radical resection of a tumor in the humerus and the femur or knee, the most common locations for such procedures. The objectives of this study were to quantify the nationwide variation in their negotiated prices along common hospital, payor, and state-policy level factors. While the specific policies analyzed are unique to the U.S., the findings on how market dynamics influence the valuation of complex surgical care have global implications for health systems navigating the challenges of cost containment and price transparency.

Methods

Overview

This is a cross-sectional study evaluating negotiated prices between hospitals and payors from the Turquoise Health (TQH) “Clear Rates” Database, which aggregates U.S. hospital-reported negotiated payor rates for common procedures by current procedural terminology (CPT) code. [21]

Inclusion criteria

CPT codes evaluated by this study included 24150 (i.e., radical resection of tumor, shaft of humerus or distal humerus) and 27365 (i.e., radical resection of tumor, femur or knee). Data were analyzed in one overall cohort containing both CPT codes, and then individually within a “radical resection of humerus” and a “radical resection of femur/knee” cohort.

For this analysis, we only included rates from the TQH database that had complete payor and regional information. Following studies with similar methodologies [15,16,20,22], we then excluded outliers within the top and bottom 10% of all negotiated rates.

Data source

Data was extracted from TQH on December 31^st, 2024, under a licensing agreement, and included procedure rate, the type of payor, total bed range of the hospital (i.e., a proxy for hospital size), hospital type, and hospital location. Data on Medicaid expansion status and CoN regulations were extracted from the Kaiser Family Foundation and the National Conference of State Legislatures using 2024 data, and was extracted on December 31^st, 2024. [23,24] CoN laws are intended to limit healthcare cost growth by restricting market entry and expansion; their association with negotiated prices in orthopaedics is a key area of continued research. [14–16] Scope of practice regulations for advanced practice providers were included as an additional proxy for market competition, which can influence overall procedural rates. For example, broader practice authority may expand access to conservative or postoperative care options, potentially impacting surgical case volumes or procedure demands. [25] Data on Nurse Practitioner (NP) independent practice regulations were extracted from the 2024 American Association of Nurse Practitioners data on July 1^st, 2025. [26] Lastly, state-mandated All-Payer Claims Database (APCD), which mandates the reporting of all public and private health claims, are a direct price transparency mechanism that may have an impact on negotiation. [27] Data on state participation in APCD initiatives were extracted from the University of New Hampshire and the National Association of Health Data Organizations database on July 1^st, 2025. [27]

Hospital and payor-level variables

Procedure rates are standardized in 2024 U.S. Dollars. Categories for the type of payor included commercial, managed Medicaid, Medicare Advantage, dual Medicare-Medicaid, exchange, Veteran Affairs (VA), and worker’s compensation. Total bed range was used as a proxy for hospital size and refers to the approximate number of inpatient hospital beds. Total bed range was defined as a categorical variable with 6 options, as reported by TQH: 1–100, 100–300, 300–500, 500–1000, 1000–1500, 1500 + . Hospital type was reported by TQH. Critical Access Hospitals (CAHs) are hospitals designated by CMS to provide care within a geographic area without other hospitals in a 35-mile radius. [28] All other hospitals were designated as acute care hospitals.

State policy variables

Medicaid expansion status, CoN regulations, and state-mandated APCD participation were all defined as binary variables (yes/no). NP scope-of-practice regulations were categorized into “full practice,” “restricted practice,” and “no practice.”

Regional variables

Nine regions were defined according to the U.S. Census Bureau using the state of the listed hospital: New England, Mid-Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain, and Pacific. [29] A full breakdown of states in each listed region is provided in the Appendix in (S1 Table).

Statistical analysis

First, a descriptive analysis was conducted to summarize cohort characteristics using frequencies and percentages for categorical variables and means with standard deviation (SD) for continuous variables. Second, a multivariate linear regression was performed to determine the influence of hospital, payor, regional, and state policy factors on negotiated rates. All regressions control for every included variable in the study. Before model finalization, we verified the assumptions of linear regression, including the linearity of independent variables and the homoscedasticity and normality of residuals. Multicollinearity was assessed using Variance Inflation Factors (VIF): all included variables demonstrated a VIF < 5, suggesting no significant collinearity between predictors. Regression coefficients represent the mean difference in U.S. dollars between the evaluated variable and the reference group. Model performance was evaluated using the adjusted R² to determine the proportion of variance in negotiated rates explained by the independent variables. Results are visualized using forest plots. A p-value ≤ 0.05 was considered statistically significant for all analyses, which were conducted in R Version 4.4.1 (The R Foundation for Statistical Computing, Vienna, Austria).

Ethics approval

This study includes publicly available data and negotiated payor rates from the Turquoise Health Database. Given that negotiated payor rates are general listed prices publicly posted by a hospital, these are not associated with patients or hospital encounters. Thus, this study was approved as exempt by our Institutional Review Board (#Pro00112768).

Results

Cohort details

A total of 285,857 negotiated payor rates were included for analysis (Table 1). This comprised 177,728 rates for radical resection of the humerus (CPT 24150) and 108,129 rates for radical resection of the femur/knee (CPT 27365). For the humerus cohort, the mean payor rate was $5,645.95 (SD: $2,626.63). For the femur/knee cohort, the mean payor rate was $5,575.91 (SD: $3,613.73). The highest percentage of rates came from the Middle Atlantic geographic region (40,233 (22.6%) for 24150; 29,566 (27.3%) for 27365).

Table 1. Overview of Included Negotiated Payor Rates for Radical Resection of Bone Tumors.

Variable	CPT 24150^a	CPT 27365^b
Number of Payor Rates	177,728	108,129
Mean Payor Rate (SD)	$5,645.95 ($2,626.63)	$5,575.91 ($3,613.73)
Total Bed Range of Hospital (N (%))
1 - 100	51,183 (28.8%)	32,753 (30.3%)
100 - 300	64,454 (36.3%)	36,928 (34.2%)
300 - 500	35,477 (20.0%)	23,151 (21.4%)
500 - 1000	22,148 (12.5%)	11,634 (10.8%)
1000 - 1500	1,731 (1.0%)	1,095 (1.0%)
1500 +	2,735 (1.5%)	2,568 (2.4%)
Hospital Type (N (%))
Acute Care	Acute Care	Acute Care
Critical Access	Critical Access	Critical Access
Payor Class (N (%))
Commercial	100,590 (56.6%)	79,224 (73.3%)
Dual	1,369 (0.8%)	259 (0.2%)
Managed Medicaid	17,264 (9.7%)	13,428 (12.4%)
Medicare Advantage	52,635 (29.6%)	13,172 (12.2%)
Veterans Affairs	3,126 (1.8%)	800 (0.7%)
Workers’ Compensation	2,744 (1.5%)	1,246 (1.2%)
U.S. Census Bureau Division
Middle Atlantic	40,233 (22.6%)	29,566 (27.3%)
New England	11,617 (6.5%)	5,225 (4.8%)
East North Central	30,973 (17.4%)	9,355 (8.6%)
East South Central	15,367 (8.6%)	8,060 (7.5%)
Mountain	6,060 (3.4%)	9,753 (9.0%)
Pacific	17,818 (10.0%)	9,221 (8.5%)
South Atlantic	30,906 (17.4%)	14,729 (13.6%)
West North Central	11,812 (6.6%)	12,654 (11.7%)
West South Central	12,942 (7.3%)	9,566 (8.8%)

Open in a new tab

Abbreviations: SD = Standard Deviation; CPT = Current Procedural Terminology Code; US = United States.

^aCPT 24150 = Radical resection of tumor, shaft of humerus or distal humerus.

^bCPT 27365 = Radical resection of tumor, femur or knee.

Most rates for both procedures came from hospitals with 100–300 beds (36.3% for humerus, 34.2% for femur/knee). Commercial payors represented the largest payor class for both cohorts, accounting for 56.6% of humerus resection rates and 73.3% of femur/knee resection rates. Rates from acute care hospitals were most common, representing 93.8% of the humerus cohort and 87.4% of the femur/knee cohort. A full overview of the payor rate characteristics is available in Table 1.

Radical resection of humerus cohort

A multivariate linear regression for the humerus resection cohort (CPT 24150) revealed several significant hospital-level factors associated with payor rates (Fig 1). Compared to hospitals with 1–100 beds, rates mostly increased with bed count and were the highest in facilities with 1500 + beds (+$797.46, p < 0.001). Conversely, rates were lower in hospitals with 500–1000 beds (-$98.48, p < 0.001). CAHs had rates that were $703.76 lower than acute care hospitals (p < 0.001).

Fig 1 — The model controls for payor class and U.S. Census Bureau region. Reference groups include hospitals with 1–100 beds (for “Beds”) and acute care facilities (for “Type”).

Payor class was also associated with price variation (Fig 2). Relative to Commercial payors, rates were higher Workers’ Compensation (+$1543.66, p < 0.001) and Medicare Advantage ($71.18, p < 0.001) plans. Rates for Managed Medicaid (-$2016.69, p < 0.001) and Veterans Affairs (-$295.97, p < 0.001) were lower compared to Commercial payors.

In the regional analysis, except for the East North Central region, all regions had higher rates in comparison to the Middle Atlantic region (Fig 3) varying between $172.04 higher (p < 0.001, East South Central) and $1,769.43 higher (p < 0.001, New England). All data for the humerus regression can be found in eAppendix in S2 Table.

A sub-analysis of health policies for the humerus resection cohort revealed that most evaluated policies were associated with higher mean payor rates compared to states without the policies (Fig 4). This included Medicaid expansion (+$274.64, p < 0.001), CoN laws (+$348.25, p < 0.001), and restricted NP practice (+$662.08, p < 0.001). On the other hand, the existence of an APCD mandate was not significantly associated with a change in rates (p = 0.97) and states with full NP practice had lower rates (-$226.29, p < 0.001). All data for the humerus cohort health policy sub-analysis can be found in eAppendix in S3 Table.

Fig 4 — Policies include Medicaid Expansion, Certificate of Need (CoN) status, nurse practitioner (NP) scope-of-practice laws, and All-Payer Claims Database (APCD) mandates. The model controls for hospital bed size, hospital type, payor class, and U.S. Census Bureau region.

Radical resection of femur/knee cohort

For the femur/knee resection cohort (CPT 27365), a multivariate linear regression identified several significant hospital-level variables impacting payor rates (Fig 5). The impact of hospital size was variable: most facility sizes were associated with lower payor rates compared with hospitals with 1–100 beds, except hospitals with 1000–1500 beds (+$1753.12, p < 0.001) and 1500 + beds (+$3409.96, p < 0.001).

Fig 5 — The model controls for payor class and U.S. Census Bureau region. Reference groups include hospitals with 1–100 beds (for “Beds”) and acute care facilities (for “Type”).

All payor classes had significantly lower rates when compared to the Commercial payor class (Fig 6), with the Medicare Advantage class having the lowest rates (-$2946.11, p < 0.001). The regional analysis also revealed significant variability (Fig 7): all regions had rates that were higher than the Middle Atlantic division, ranging from +$252.58 (p < 0.001, West South Central) to $3,791.18 (p < 0.001, South Atlantic). All data for the femur/knee regression can be found in eAppendix in S4 Table.

Fig 7 — ” The model controls for hospital bed size, hospital type, and payor class. The reference group is “Middle Atlantic.”.

The health policy sub-analysis for the femur/knee cohort also found that most evaluated policies were associated with higher mean payor rates compared to states without such policies (Fig 8). This included Medicaid expansion (+$1399.41, p < 0.001), CoN laws (+$667.98, p < 0.001), restricted NP practice (+$1563.49, p < 0.001), and APCD mandates (+1231.24, p < 0.001). Conversely, rates were lower in states with full practice (-$250.79, p < 0.001). All data for the femur/knee cohort health policy sub-analysis can be found in eAppendix in S5 Table.

Fig 8 — Policies include Medicaid Expansion, Certificate of Need (CoN) status, nurse practitioner (NP) scope-of-practice laws, and All-Payer Claims Database (APCD) mandates. The model controls for hospital bed size, hospital type, payor class, and U.S. Census Bureau region.

Discussion

To our knowledge, this is the first study to leverage a national price transparency database to investigate the negotiated payor rates for radical resection of bone tumors. Our analysis of over 285,000 negotiated rates for radical resection procedures of the humerus and femur/knee reveals significant price variation driven by hospital, payor, regional, and state-level policy factors. These findings are similar to previous studies that identify large, often unexplained variations in negotiated payor rates. [14,15,17,18] For patients with primary or metastatic bone disease, who often require complex and costly care, this variability raises important questions about health equity in a specialized field where patient choice may be limited by geography and clinical expertise.

A primary finding of this study is that hospital size is a significant driver of negotiated rates. For both procedures, the largest hospitals (1000 + beds) had substantially higher prices than smaller facilities. This is particularly pronounced for femur/knee resections, where rates at the very largest hospitals were approximately $3400 higher than at the smallest. This may reflect the market power of large hospitals, often with academic sarcoma centers that can handle complex cases and consequently may have greater leverage in negotiations with payors. [30,31] Given that most orthopaedic oncologists who perform these procedures routinely are located in academic medical centers, these findings may mean patients and health systems have no choice but to pay for the higher-cost procedures, effectively creating a price-inelastic market where transparency alone is unlikely to foster competition or reduce costs. Additionally, although prior studies have found that larger hospitals have higher prices than smaller hospitals [30,31], we observed a non-linear trend in the present study, with mid-sized hospitals (300–1000 beds) having inconsistently high or low rates compared to both the smallest and largest facilities. This may suggest a competitive disadvantage for these institutions, which may lack the negotiating power of large systems (e.g., those with academic sarcoma centers). However, when considering hospital type, CAHs had significantly lower rates for both procedures. The lower rate for the humerus procedure at CAHs may align with expectations of lower-case volume or complexity in rural settings. [32] Together, these findings suggest that market power, whether in the context of specialized expertise of large academic centers or the geographic monopoly of small rural CAHs, may be a driver of high prices for specialized orthopaedic oncology care.

Our analysis also uncovered regional differences in negotiated rates that persist even after controlling for hospital, payor, and state-policy factors. Using the Middle Atlantic (i.e., New York, New Jersey, Pennsylvania) as the reference, nearly all other U.S. regions had significantly higher prices for both procedures. The variation was particularly pronounced for femur/knee resections, where rates in the South Atlantic (e.g., North Carolina, South Carolina, Virginia) were nearly $3,800 higher, and rates in the West North Central region (e.g., North Dakota, South Dakota, Minnesota) were over $3,100 higher. Notably, the East North Central region was a significant outlier, with humerus resection rates nearly $1,000 lower than in the Middle Atlantic. Such large geographic variations may reflect underlying differences in regional market concentration, the negotiating leverage of dominant hospital systems in certain areas, or cost-of-living differences not fully captured by other variables.

The influence of payor type also revealed significant variability. Medicaid plans were associated with the lowest payor rates for both procedures. This finding is consistent with prior literature in orthopaedics, and may reflect limits on Medicaid reimbursement. [33,34] While lower rates are likely beneficial to patients with reduced cost-sharing, it is also important to ensure lower prices do not disincentivize providers from offering the service. For example, in neurosurgical populations, lower reimbursement rates for common procedures were associated with lower provider enrollment. [35] Future studies should evaluate access to orthopaedic oncology care in populations with Medicaid insurance to ensure that such patients are not facing additional barriers out of the scope of this study. Other insurance subtypes also showed unclear variability. For humerus resections, Medicare Advantage plans had higher rates than commercial plans (+$71), while for femur/knee resections, they paid significantly less (-$2,946). These differences suggest that the balance of power between a given hospital and a given Medicare Advantage plan can lead to vastly different prices for similar services. Taken together, these wide variations by insurer reveal a variable pricing system where the cost of essential cancer surgery is determined not only by clinical value, but also by the specific negotiating power and policies of a patient’s insurance plan. However, whether this variability translates to differences in patient outcomes is a critical, yet unknown, question in orthopaedic oncology. In one gastrointestinal surgical cancer population, Sankaran et al found that despite high rate variation between similar providers, the median payor rate was not associated with mortality or major postoperative complications. [18] Future studies are needed to understand the relationship between payor-driven variation in procedure costs, access to care, and outcomes for orthopaedic oncology patients.

At the state policy level, our findings were often counterintuitive. The presence of CoN laws, which are intended to control costs by limiting market entry and ensuring a healthcare “need”, were associated with significantly higher prices for both procedures. This finding conflicts with other studies in orthopaedic surgery, that found associations between CoN laws and lower prices. [13,15,16] In the context of orthopaedic oncology care, which already may face higher prices due to hospital-level factors, it is possible that CoN laws may inadvertently grant preexisting hospitals greater market power, allowing them to negotiate higher rates. Because the market for radical bone tumor resection is already limited to a small number of tertiary care centers with the requisite multidisciplinary expertise and infrastructure, CoN laws may effectively shield these centers from competition. This may further increase prices and limit access to care for patients who need radical resections.

Medicaid expansion was linked to higher negotiated rates, consistent with prior research. [13,15,16] However, state-mandated APCDs, a direct price transparency mechanism, were not associated with humerus resection payor rates but were associated with $1,231 higher payor rates for femur/knee resections. In the case of radical resection of bone tumors, this contradicts the logic that transparency should foster competition and lower prices. This paradox may be specific to high-acuity, low-volume procedures where patient choice is driven by clinical expertise rather than cost. For example, a patient with a bone sarcoma may decide where to seek care based on the reputation of the surgical team and the geographic feasibility, rather than by comparing listed prices. [36] In this price-inelastic market, transparency may not empower patients to “shop” for lower prices, but instead may allow hospitals to adjust their own rates upward toward those of their high-cost peers knowing that patients will be restricted to their institutions, leading to an inflationary effect. Thus, in the radical bone tumor resection market, it is possible that transparency may allow lower-priced providers to increase prices to the market price, which may be inflated at baseline by the state- and hospital-level factors mentioned above.

The findings for NP scope of practice laws have implications for understanding the role of advanced practice providers in specialized surgical markets. For humerus resections, broader NP practice authority was linked to lower surgical rates, supporting the theory that an expanded workforce could increase competition or access to non-operative care. [25] Yet, for femur/knee resections, the opposite was true, with broader NP practice linked to significantly higher rates. This discrepancy may indicate that the role of advanced practice providers differs by procedure complexity and should be explored in future research. It is possible that these differences reflect differences in NP role based on tumor location. In the lower extremity, resections are often more likely to be essential due to lower extremity weight bearing requirements. For patients with upper extremity tumors, is possible that access to NPs can result in alternatives to surgery, such as injections and counseling about weight bearing limitations. On the contrary, the same access to NPs for patients with lower extremity lesions could result in more rapid facilitation of a surgical intervention.

While this analysis is specific to the multi-payer healthcare system of the U.S., health systems worldwide, whether single-payer or market-based, are grappling with the challenge of controlling costs for specialized oncologic care, while ensuring access and quality. [37–39] For example, even in universal healthcare systems (e.g., Canada) where the government is permitted to negotiate the price of cancer treatments with manufacturers and direct patient-facing medical costs are lower, patients still experience high levels of financial distress related to their cancer care. [40,41] Our findings serve as a case study for variable market dynamics, demonstrating that well-intentioned policies like market entry regulation (CoN laws) and price transparency mandates can have counterintuitive, and even inflationary, effects on prices. For international policymakers considering market-based reforms or greater price disclosure, this study highlights that simply revealing prices or limiting provider supply does not guarantee cost containment. Further efforts are needed to determine the best way to limit costs. The complex interplay between provider access, regulatory frameworks, and negotiating power can subvert policy goals, which should be kept in mind when establishing fair and predictable pricing for complex medical procedures.

This study has several limitations. Most importantly, the prices included in this study account for the resection portion of the procedure, but they do not account for any reconstructive portion(s). In clinical practice, reconstruction is an integral and often more resource-intensive component of limb-sparing surgery than the resection itself. The reconstructive portion may also have its own significant price variability associated with it due to market forces that drive implant prices. Consequently, the price variability observed in our study may not be representative of the complete episode of care associated with the surgical treatment of bone tumors. Future studies should incorporate all elements of bone tumor resection surgery to develop a complete cost profile for patients undergoing these procedures, including grafts, implants, and custom endoprostheses. Second, data were extracted from TQH, relying on the accuracy of each institution’s disclosed negotiated rates. Hospitals or insurers may report only partial or grouped costs, potentially underestimating or overestimating total procedure expenses. Similarly, an additional limitation of this data source is that it includes rates from all hospitals, irrespective of whether they actually perform these complex procedures. Many smaller hospitals may list a negotiated rate for these CPTs but never perform the operation. This is particularly relevant for the interpretation of findings related to small hospitals and Critical Access Hospitals, whose negotiated rates may be theoretical rather than based on actual clinical volume. Third, this analysis only considers negotiated rates and does not account for variations in true reimbursement rates or direct out-of-pocket costs incurred by patients. Fourth, this study did not incorporate clinical outcomes (e.g., surgical complication rates) or patient-reported outcomes (e.g., financial toxicity or quality-of-life measures), which would be necessary to gauge the true value and cost-efficacy of each intervention in the long-term. Finally, our analysis was limited to two CPT codes for radical resection of long bones, and the findings may not be generalizable to other oncologic procedures or anatomical locations. While this approach was chosen to allow for a direct evaluation of two common procedures, future studies should investigate whether these pricing dynamics hold true for other surgical oncologic procedures.

Conclusions

In conclusion, the price of radical bone tumor resection in the U.S. shows significant, often counterintuitive variability and may be driven in part by market dynamics. These findings underscore the urgent need for more granular data to understand these price drivers, which is essential for developing a more rational, predictable, and equitable pricing system for vital cancer care.

Supporting information

S1 Table. State Divisions According to United States Census Bureau.

(DOCX)

pone.0343676.s001.docx^{(20.4KB, docx)}

S2 Table. Multivariable Linear Regression for Payor Rates within the Radical Resection of Humerus Cohort.

(DOCX)

pone.0343676.s002.docx^{(18.6KB, docx)}

S3 Table. Multivariable Linear Regression for Payor Rates within the Radical Resection of Humerus Cohort, Health Policy Sub-Analysis.

(DOCX)

pone.0343676.s003.docx^{(17.2KB, docx)}

S4 Table. Multivariable Linear Regression Results for Payor Rates within the Radical Resection of Femur/Knee Cohort.

(DOCX)

pone.0343676.s004.docx^{(18.6KB, docx)}

S5 Table. Multivariable Linear Regression Results for Radical Resection of Femur/Knee Cohort, Health Policy Sub-Analysis.

(DOCX)

pone.0343676.s005.docx^{(17.2KB, docx)}

Abbreviations

CPT: Current Procedural Terminology
CoN: Certificate of Need
APCD: All-Payer Claims Database
NP: Nurse Practitioner
CMS: Centers for Medicare & Medicaid Services
TQH: Turquoise Health
VA: Veteran Affairs
CAH: Critical Access Hospitals
SD: Standard Deviation
IQR: Interquartile Range.

Data Availability

Payor information obtained from Turquoise Health: https://turquoise.health. Medicaid and regulatory information extracted from Kaiser Family Foundation and National Conference of State Legislatures: https://www.kff.org/affordable-care-act/state-indicator/state-activity-around-expanding-medicaid-under-the-affordable-care-act/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D, https://www.ncsl.org/health/certificate-of-need-state-laws NP independent practice regulatory data from American Association of Nurse Practitioners Data: https://www.aanp.org/advocacy/state/state-practice-environment All-Payer Claims Database data from University of New Hampshire and National Association of Health Data Organizations: https://www.apcdcouncil.org/state-efforts/apcd-legislation-state Additional data availability information: 1. Negotiated rate (primary outcome), payor type, total bed range of hospital, hospital type, hospital location: Obtained from the Turquoise Health “Clear Rates” database under a licensing agreement. Further information can be found at https://turquoise.health/products/clear_rates_data. Researchers interested in working with this dataset can contact Turquoise Health via https://turquoise.health/contact?page=contact-us. 2. Medicaid expansion status: obtained from a publicly-available source via the Kaiser Family on December 31st, 2024: https://www.kff.org/affordable-care-act/state-indicator/state-activity-around-expanding-medicaid-under-the-affordable-care-act/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D 3. Certificate of Need status: Data was obtained from a publicly-available source via the National Conference of State Legislatures on December 31st, 2024: https://www.ncsl.org/health/certificate-of-need-state-laws 4. NP scope-of-practice regulatory laws: Data was obtained from a publicly-available source via the American Association of Nurse Practitioners on July 1st, 2025: https://www.aanp.org/advocacy/state/state-practice-environment 5. All-Payer Claims Database (APCD) state participation: Data was obtained from a publicly-available source via the University of New Hampshire and National Association of Health Data Organizations on July 1st, 2025: https://www.apcdcouncil.org/state-efforts/apcd-legislation-state 6. U.S. Census Bureau Region: Data was obtained from a publicly-available source via the U.S. Census Bureau at https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf.

Funding Statement

The author(s) received no specific funding for this work.

References

1.Bartelstein MK, Boland PJ. Fifty years of bone tumors. J Surg Oncol. 2022;126(5):906–12. doi: 10.1002/jso.27027 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Gazendam A, Ghert M. What’s new in musculoskeletal tumor surgery. J Bone Joint Surg Am. 2023;105(24):1929–36. doi: 10.2106/JBJS.23.00833 [DOI] [PubMed] [Google Scholar]
3.Errani C, Mavrogenis AF, Tsukamoto S. What’s new in musculoskeletal oncology. BMC Musculoskelet Disord. 2021;22(1):704. doi: 10.1186/s12891-021-04590-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Meltzer PS, Helman LJ. New horizons in the treatment of osteosarcoma. N Engl J Med. 2021;385(22):2066–76. doi: 10.1056/NEJMra2103423 [DOI] [PubMed] [Google Scholar]
5.Ryan C, Stoltzfus KC, Horn S, Chen H, Louie AV, Lehrer EJ, et al. Epidemiology of bone metastases. Bone. 2022;158:115783. doi: 10.1016/j.bone.2020.115783 [DOI] [PubMed] [Google Scholar]
6.Franchi A. Epidemiology and classification of bone tumors. Clin Cases Miner Bone Metab. 2012;9(2):92–5. [PMC free article] [PubMed] [Google Scholar]
7.Schulman KL, Kohles J. Economic burden of metastatic bone disease in the U.S. Cancer. 2007;109(11):2334–42. doi: 10.1002/cncr.22678 [DOI] [PubMed] [Google Scholar]
8.Morya VK, Magar AG, Park S-H, Noh K-C. Systemic strategies for osteosarcoma: advances and future directions. Discov Oncol. 2025;16(1):1367. doi: 10.1007/s12672-025-02208-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Sonkin D, Thomas A, Teicher BA. Cancer treatments: Past, present, and future. Cancer Genet. 2024;286–287:18–24. doi: 10.1016/j.cancergen.2024.06.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Bläsius F, Delbrück H, Hildebrand F, Hofmann UK. Surgical treatment of bone sarcoma. Cancers (Basel). 2022;14(11):2694. doi: 10.3390/cancers14112694 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Hayashi K, Tsuchiya H. The role of surgery in the treatment of metastatic bone tumor. Int J Clin Oncol. 2022;27(8):1238–46. doi: 10.1007/s10147-022-02144-6 [DOI] [PubMed] [Google Scholar]
12.Xu M, Wang Z, Yu X-C, Lin J-H, Hu Y-C. Guideline for limb-salvage treatment of osteosarcoma. Orthop Surg. 2020;12(4):1021–9. doi: 10.1111/os.12702 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Kutzer KM, Kiwinda LV, Yang D, Mitchell JK, Luo EJ, Harman EJ, et al. Insurance Payor status and outcomes in pediatric sports-related injuries: A rapid review. Clin Pract. 2025;15(3):52. doi: 10.3390/clinpract15030052 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Kutzer KM, Wu KA, Shenoy DA, Therien AD, Shah SN, Ryan SP, et al. State health care regulations and political affiliation associated with total hip arthroplasty prices across the United States. J Arthroplasty. 2025;40(8S1):S30–5. doi: 10.1016/j.arth.2025.04.007 [DOI] [PubMed] [Google Scholar]
15.Wu KA, Baker RL, Hurley ET, Kutzer KM, Choudhury A, Pean CA, et al. Health care policy and arthroscopic rotator cuff repair prices in the United States. J Shoulder Elbow Surg. 2025;34(1):368–74. doi: 10.1016/j.jse.2024.07.043 [DOI] [PubMed] [Google Scholar]
16.Wu KA, Kutzer KM, Doyle TR, Hurley ET, Pean CA, Anakwenze O, et al. The impact of political partisanship, certificate of need, Medicaid expansion, and area deprivation index on total shoulder arthroplasty prices in the United States. J Shoulder Elbow Surg. 2025;34(1):361–7. doi: 10.1016/j.jse.2024.05.051 [DOI] [PubMed] [Google Scholar]
17.Kasthuri VS, Alsoof D, Balmaceno-Criss M, Daher M, McDonald CL, Diebo BG, et al. Variability in expenses related to spine oncology care: comparison of payer-negotiated rates at National Cancer Institute-Designated Cancer Centers. Spine J. 2024;24(2):304–9. doi: 10.1016/j.spinee.2023.10.008 [DOI] [PubMed] [Google Scholar]
18.Sankaran R, O’Connor J, Nuliyalu U, Diaz A, Nathan H. Payer-negotiated price variation and relationship to surgical outcomes for the most common cancers at NCI-Designated cancer centers. Ann Surg Oncol. 2024;31(7):4339–48. doi: 10.1245/s10434-024-15150-x [DOI] [PubMed] [Google Scholar]
19.Hospital Price Transparency Rule. https://www.cms.gov/priorities/key-initiatives/hospital-price-transparency. 2021. Accessed 2025 March 7. [Google Scholar]
20.Shenoy DA, Therien AD, Poehlein E, Zirbes C, Kutzer K, Wu KA, et al. Hospital and regional-level factors associated with negotiated payer rates for hip fracture fixation: A multistate analysis. Value Health. 2026;29(2):206–12. doi: 10.1016/j.jval.2025.09.3051 [DOI] [PubMed] [Google Scholar]
21.Clear Rates Data: Price Transparency Data at Your Fingertips: Turquoise Health; [cited 2024 https://turquoise.health/products/clear_rates_data] [Google Scholar]
22.Shenoy DA, Parsons KE, Wu KA, Therien AD, Zhang Y, Hayes A, et al. Hospital, regional, and policy-level variations in shoulder arthroplasty prices: A Nationwide database study. JSES Rev Rep Tech. 2025;6(1):100601. doi: 10.1016/j.xrrt.2025.100601 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Kaiser Family Foundation. Status of state action on the Medicaid expansion decision. 2024. https://www.kff.org/affordable-care-act/state-indicator/state-activity-around-expanding-medicaid-under-the-affordable-care-act/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D [Google Scholar]
24.Certificate of Need State Laws. https://www.ncsl.org/health/certificate-of-need-state-laws. 2024. Accessed 2025 June 29. [Google Scholar]
25.Hollenbeck BK, Kaufman SR, Oerline M, Modi PK, Caram MEV, Shahinian VB, et al. Effects of Advanced practice providers on single-specialty surgical practice. Ann Surg. 2023;277(1):e40–5. doi: 10.1097/SLA.0000000000004846 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.American Association of Nurse Practitioners. State practice environment. https://www.aanp.org/advocacy/state/state-practice-environment. 2024. Accessed 2025. [Google Scholar]
27.Institute for Health Policy and Practice (IHPP) at the U of NH (UNH). All-Payer Claims Database Legislation by State. National Association of Health Data Organizations (NAHDO). 2024. https://www.apcdcouncil.org/state-efforts/apcd-legislation-state [Google Scholar]
28.Joynt KE, Harris Y, Orav EJ, Jha AK. Quality of care and patient outcomes in critical access rural hospitals. JAMA. 2011;306(1):45–52. doi: 10.1001/jama.2011.902 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Census Regions and Divisions of the United States. https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf. 2025. Accessed 2025 October 1. [Google Scholar]
30.White C, Reschovsky JD, Bond AM. Understanding differences between high- and low-price hospitals: Implications for efforts to rein in costs. Health Aff (Millwood). 2014;33(2):324–31. doi: 10.1377/hlthaff.2013.0747 [DOI] [PubMed] [Google Scholar]
31.Beaulieu ND, Chernew ME, McWilliams JM, Landrum MB, Dalton M, Gu AY, et al. Organization and Performance of US Health Systems. JAMA. 2023;329(4):325–35. doi: 10.1001/jama.2022.24032 [DOI] [PubMed] [Google Scholar]
32.Schaefer SL, Ibrahim AM. Overcoming geographic barriers: Surgical care in rural populations. Clin Colon Rectal Surg. 2024;38(1):41–8. doi: 10.1055/s-0044-1786390 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Henderson AP, Moore ML, Holle AM, Haglin JM, Brinkman JC, Van Schuyver PR, et al. Medicaid Reimbursement for Total Hip and Knee Arthroplasty: A State-by-State Analysis Compared With Medicare. J Arthroplasty. 2025;40(2):320–7. doi: 10.1016/j.arth.2024.08.021 [DOI] [PubMed] [Google Scholar]
34.Moore ML, Henderson A, Haglin JM, Brinkman JC, Van Schuyver PR, Bingham JS, et al. Medicaid reimbursement for common orthopaedic trauma procedures is 16.0% less compared with medicare with substantial variability between states. J Orthop Trauma. 2024;38(11):e362–70. doi: 10.1097/BOT.0000000000002901 [DOI] [PubMed] [Google Scholar]
35.Rallo MS, Berke CN, Shaw TC, Bunch JA, Menger RP. State-to-state variation in medicaid reimbursement for neurosurgical procedures: Implications for healthcare access in vulnerable populations. Neurosurgery. 2024;:10.1227/neu.0000000000002823. doi: 10.1227/neu.0000000000002823 [DOI] [PubMed] [Google Scholar]
36.Ejaz A, Spolverato G, Bridges JF, Amini N, Kim Y, Pawlik TM. Choosing a cancer surgeon: Analyzing factors in patient decision making using a best-worst scaling methodology. Ann Surg Oncol. 2014;21(12):3732–8. doi: 10.1245/s10434-014-3819-y [DOI] [PubMed] [Google Scholar]
37.Voda AI, Bostan I. Public health care financing and the costs of cancer care: A cross-national analysis. Cancers (Basel). 2018;10(4):117. doi: 10.3390/cancers10040117 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Shih Y-CT, Ganz PA, Aberle D, Abernethy A, Bekelman J, Brawley O, et al. Delivering high-quality and affordable care throughout the cancer care continuum. J Clin Oncol. 2013;31(32):4151–7. doi: 10.1200/JCO.2013.51.0651 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Niraula S. Strategizing health technology assessment for containment of cancer drug costs in a universal health care system: Case of the pan-Canadian Oncology Drug Review. Cancer. 2019;125(18):3100–3. doi: 10.1002/cncr.32209 [DOI] [PubMed] [Google Scholar]
40.Longo CJ, Fitch MI, Banfield L, Hanly P, Yabroff KR, Sharp L. Financial toxicity associated with a cancer diagnosis in publicly funded healthcare countries: A systematic review. Support Care Cancer. 2020;28(10):4645–65. doi: 10.1007/s00520-020-05620-9 [DOI] [PubMed] [Google Scholar]
41.Fitch MI, Longo CJ, Chan RJ. Cancer patients’ perspectives on financial burden in a universal healthcare system: Analysis of qualitative data from participants from 20 provincial cancer centers in Canada. Patient Educ Couns. 2021;104(4):903–10. doi: 10.1016/j.pec.2020.08.013 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0343676.r001

Decision Letter 0

Xiaoen Wei

5 Oct 2025

Dear Dr. Bhat,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Nov 19 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Xiaoen Wei

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1.Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for uploading your study's underlying data set. Unfortunately, the repository you have noted in your Data Availability statement does not qualify as an acceptable data repository according to PLOS's standards.

At this time, please upload the minimal data set necessary to replicate your study's findings to a stable, public repository (such as figshare or Dryad) and provide us with the relevant URLs, DOIs, or accession numbers that may be used to access these data. For a list of recommended repositories and additional information on PLOS standards for data deposition, please see https://journals.plos.org/plosone/s/recommended-repositories .

3. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

[Note: HTML markup is below. Please do not edit.]

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

Reviewer #1: The manuscript takes on an important but often overlooked issue: the drivers of price variation in radical bone tumor resections across the United States. The authors leverage a large national dataset and apply a thoughtful multivariate regression framework to explore hospital, payor, and state-policy factors shaping negotiated rates. From a policy perspective, this is a timely and highly relevant question, because the costs of oncologic surgery are escalating and transparency laws were designed precisely to illuminate such variability. I appreciate that the study highlights differences not only by hospital size and type but also by Medicaid expansion, Certificate of Need laws, and other state-level regulations. These are all important levers that could, in theory, guide cost containment strategies. The sheer scale of data (nearly 300,000 negotiated rates) is impressive and gives the study statistical power that most analyses of this type lack.

The most pressing issue is that the introduction jumps straight into orthopaedic oncology and cost transparency without giving readers a strong foundation in cancer biology and treatment strategies. Since this is ultimately a cancer surgery study, the narrative should begin with a broader perspective on the burden of cancer and the evolution of treatment approaches. For example, I recommend citing the review by D. Sonkin and A. Thomas, “Cancer Treatments: Past, Present, and Future” (2024), which was authored by the Chief of the US National Cancer Institute and provides a solid historical and clinical context for why access to surgery remains central even in the age of targeted therapies and immunotherapy. This would anchor the cost discussion within the larger story of cancer care.

In addition, the discussion could benefit from a deeper engagement with how financial variation intersects with equity and outcomes in cancer treatment. Right now, the paper notes that large hospitals and certain policy environments drive higher prices, but it does not really reflect on what this means for patients who require limb-sparing surgery for osteosarcoma or metastatic disease. It would strengthen the argument to cite and briefly discuss recent literature on cancer care costs globally. Similarly, the section on Medicaid reimbursement and access might draw on papers that show how reimbursement disparities can directly affect surgical oncology enrollment and availability. Without tying these policy findings back to patient outcomes, the paper risks reading like a pure health economics exercise rather than a piece of cancer policy research.

Another point that needs attention is the interpretation of Certificate of Need and All-Payer Claims Database findings. The current manuscript highlights counterintuitive associations (e.g., CoN laws linked to higher rates), but the discussion does not fully engage with why these paradoxes might occur in the specific context of bone tumor surgery. One way to frame this is through the lens of unintended policy consequences, as transparency laws or market-entry restrictions may actually consolidate market power. Bringing in the broader oncology policy discussion, this would help place these observations in a larger framework of how reforms sometimes fail to achieve their intended cost-control effects.

Lastly, the paper would benefit from more clarity on limitations. It is noted that reconstructive procedures are not captured, but this is a major caveat, because in real-world bone tumor surgery, reconstruction is often more resource-intensive than resection itself. That omission could substantially change the cost profile, and readers need a fuller acknowledgement of this. The writing also occasionally becomes too descriptive, listing results without embedding them into a coherent argument. I encourage the authors to rework the discussion so that each section not only reports the findings but also interprets them in light of cancer care delivery, patient equity, and future policy design.

Reviewer #2: Review of Manuscript PONE-D-25-44873

Title: Exploring the Drivers of Price Variation in Radical Bone Tumor Resection: A Nationwide Database Study

Authors: Devika A. Shenoy et al.

General Assessment

This manuscript presents a timely and important analysis of price variation in radical bone tumor resections using a large national dataset derived from the Turquoise Health database. The authors investigate negotiated payor rates for CPT codes 24150 and 27365, examining associations with hospital characteristics, payor types, and state-level health policies. The study is well-structured, methodologically sound, and contributes to the literature on healthcare cost transparency and orthopaedic oncology.

Strengths

1. Novelty and Relevance:

The study addresses a gap in the literature by focusing on specialized oncologic procedures, which are often excluded from broader cost analyses. The use of price transparency data for radical bone tumor resections is novel.

2. Robust Dataset:

The analysis includes over 285,000 negotiated rates, providing strong statistical power and generalizability across U.S. hospitals.

3. Methodological Rigor:

The use of multivariate linear regression and stratified analyses by CPT code is appropriate and well-executed. The authors control for multiple confounders and present confidence intervals and p-values clearly.

4. Policy Implications:

The findings have implications for healthcare policy, particularly regarding the unintended consequences of Certificate of Need laws and price transparency mandates.

5. Clarity and Organization:

The manuscript is well-written, with clear tables and logical flow from introduction to discussion.

Suggestions/Concerns

1. Scope of Procedure Pricing:

The analysis focuses solely on the resection portion of the procedure, excluding reconstructive components. This limitation is acknowledged but warrants further emphasis, as it may significantly affect total cost estimates and policy implications. Would the order of the CPT codes being submitted potentially influence the charges for the resection CPT specifically? I also think this limitation makes it challenging for a reader to understand the actual total costs/charges for the operation. If there is more or less variation in the reconstruction portions of the procedures, it could negate or widen the variation. Granted there are a lot of reconstructive options, but could a subanalysis be done for those who have a distal femur replacement? A proximal femur replacement? I’d also be curious what proportion of the procedures have a 22 Modifier submitted with them.

2. Data Source Limitations:

While the Turquoise Health database is a valuable resource, the potential for incomplete or inconsistent reporting by hospitals should be discussed more thoroughly, especially regarding the reliability of negotiated rates. It would be great if possible to only include those hospitals who actually billed at least one of those procedures. I’d be curious how many of these resections are happening at particularly small hospitals, or if the costs at hospitals who actually perform these operations regularly are higher or lower than those that do not do these procedures. Given this are largely tertiary care type procedures, there may be many hospitals where neither of these CPTs is utilized in a given year, making the negotiated rate rather unimportant. Those hospitals with under 100 beds may not do these procedures, making that hospital less keen to negotiate for a higher rate. I think this limitation needs to be made more clear in the limitations section.

Is it possible to evaluate by region using this database? It would be interesting to know whether certain US regions (by US Census subgroups, for example) are significantly different.

3. Discussion

The discussion mentions that for patients with primary or metastatic disease the lack of price predictability may create uncertainty. For nearly all patients in this condition, the cost of their operation is but a small part of the price of their overall care. I do not think this conclusion is justified by the data presented. You’d have to show that the cost of the resection (and reconstruction) constitute a major part of the total cost of care. I think this sentence should be omitted.

I think the paragraph about higher cost at larger hospitals should reflect that there is no direct data here that I saw suggesting that these procedures actually happen at smaller hospitals. It would be really valuable to have some data to that effect. While this is a potential issue, it may be a small or a non-issue if few of these operations are actually happening at small hospitals.

Similarly, it is hard to place context for the findings on critical access hospitals, as we are not at all sure how often these operations happen at critical access hospital. At least in my area, I think any of these patients who might benefit from one of these operations would be transferred to another hospital.

Recommendation

Revision

With suggestions incorporated as above, I think this paper could highlight that the negotiated rates are highly variable. Recognizing that these are negotiated rates, which do not necessarily reflect where/if these procedures are happening, would be an improvement. It would also be useful to understand if this degree of variability is similar to or different to the variability for more common procedures. As in: is this a problem throughout US healthcare, or is somehow the problem more of an issue for humerus/femur bone resections.

Reviewer #3: The manuscript is an interesting attempt to pull together findings in cancer biology and treatment, but in its current form it doesn’t quite reach the level of depth and clarity needed for publication. The topic itself is certainly important and timely—there is ongoing demand for reviews that can bridge the rapidly expanding mechanistic understanding of cancer with the clinical strategies that are actually used to treat patients. However, I found the structure uneven, the integration of key literature incomplete, and the narrative a bit disjointed at times. With substantial revision, I think the article could become a useful resource, but at present it feels more like a rough draft than a polished review.

One of the first issues is the introduction. It jumps straight into specific pathways and mechanisms without giving readers a proper grounding in the broader landscape of cancer biology and therapy. For a review article, especially one aimed at a general oncology readership, the introduction really needs to set the scene more cohesively. I would strongly suggest opening with a section that lays out the major hallmarks of cancer, common therapeutic approaches (surgery, chemotherapy, targeted therapy, immunotherapy), and the historical arc of how these approaches developed. A very useful reference for this would be the article “Cancer Treatments: Past, Present, and Future,” which was written by the Chief of the US National Cancer Institute. This piece not only provides a clinical and historical context but also helps readers appreciate how far the field has come and why the new directions under discussion are significant. Integrating that into the early part of the manuscript would immediately make the framing much stronger.

In terms of organization, the review currently mixes together background concepts, detailed pathway discussions, and therapeutic implications without clear transitions. I’d recommend restructuring it into three or four distinct sections: first, a proper overview of cancer biology (with an emphasis on hallmarks and recent conceptual advances); second, mechanistic insights at the molecular or cellular level (ROS, metabolic rewiring, DNA damage repair, etc.); third, therapeutic translation, where you can explicitly connect the mechanistic findings to how they influence treatment responses, drug resistance, or immunotherapy strategies; and finally, a perspective or future outlook section that synthesizes the content rather than just restating it. Right now, the paper lacks that “arc” which allows a reader to follow the story logically from biology to clinic.

There are also places where the discussion feels somewhat superficial. For example, when talking about oxidative stress, the manuscript gestures at its dual roles but doesn’t really unpack the mechanistic details or the controversies in the field. A good review should not shy away from unresolved debates. The paper could benefit from citing more primary work in these sections to demonstrate awareness of ongoing research directions. Similarly, the section on therapeutic resistance is thin. This is an area where readers would expect a careful breakdown—how resistance mechanisms differ between chemotherapy and immunotherapy, what molecular pathways are most implicated, and how novel strategies are trying to overcome them. Even just adding a few detailed examples of clinical trials or preclinical models would enrich this section.

The figures are another weak point. They are quite basic and, in some cases, too schematic to add real value. If the authors are going to include figures, they should make them work harder for the paper: pathway diagrams that integrate multiple signals at once, or conceptual “maps” that show how tumor biology interfaces with therapeutic interventions, would be much more effective. At the moment, the visuals feel more like placeholders.

I also noticed a few language issues—typos here and there, slightly awkward phrasing, and sometimes an overuse of jargon without adequate definition. While not fatal, these things make the paper harder to read and contribute to the impression of incompleteness. A careful language polish would be beneficial, ideally by a native speaker or professional editing service.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: Yes: Li Zheng

Reviewer #2: No

Reviewer #3: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

PLoS One. 2026 Feb 26;21(2):e0343676. doi: 10.1371/journal.pone.0343676.r002

Author response to Decision Letter 1

16 Nov 2025

Reviewer #1, Comment #1: The manuscript takes on an important but often overlooked issue: the drivers of price variation in radical bone tumor resections across the United States. The authors leverage a large national dataset and apply a thoughtful multivariate regression framework to explore hospital, payor, and state-policy factors shaping negotiated rates. From a policy perspective, this is a timely and highly relevant question, because the costs of oncologic surgery are escalating and transparency laws were designed precisely to illuminate such variability. I appreciate that the study highlights differences not only by hospital size and type but also by Medicaid expansion, Certificate of Need laws, and other state-level regulations. These are all important levers that could, in theory, guide cost containment strategies. The sheer scale of data (nearly 300,000 negotiated rates) is impressive and gives the study statistical power that most analyses of this type lack.

• Author Response: We thank Reviewer #1 for their positive feedback and for recognizing the importance of this topic and the strength of our dataset.

Reviewer #1, Comment #2: The most pressing issue is that the introduction jumps straight into orthopaedic oncology and cost transparency without giving readers a strong foundation in cancer biology and treatment strategies. Since this is ultimately a cancer surgery study, the narrative should begin with a broader perspective on the burden of cancer and the evolution of treatment approaches. For example, I recommend citing the review by D. Sonkin and A. Thomas, “Cancer Treatments: Past, Present, and Future” (2024), which was authored by the Chief of the US National Cancer Institute and provides a solid historical and clinical context for why access to surgery remains central even in the age of targeted therapies and immunotherapy. This would anchor the cost discussion within the larger story of cancer care.

• Author Response: This is an excellent point. We agree that providing a broader context on the role of surgery in modern cancer care will strengthen the manuscript's foundation. We have revised the introduction (Page 4, Lines 58-61) to include a discussion on the evolution of cancer treatments and the enduring importance of surgical resection, citing the suggested review.

Reviewer #1, Comment #3: In addition, the discussion could benefit from a deeper engagement with how financial variation intersects with equity and outcomes in cancer treatment. Right now, the paper notes that large hospitals and certain policy environments drive higher prices, but it does not really reflect on what this means for patients who require limb-sparing surgery for osteosarcoma or metastatic disease. It would strengthen the argument to cite and briefly discuss recent literature on cancer care costs globally.

• Author Response: Thank you for this insightful suggestion. We have expanded the discussion to better connect our findings on price variation to potential implications for patient equity and access to care (Page 15, Lines 234-238). We have also more specifically incorporated the literature around patient outcomes in the discussion (Page 16-17, Lines 284-289). and limitations. Additionally, we have expanded on our paragraph that contextualizes our findings within the global challenge of managing oncologic care costs by providing examples of difficulties with cost control in other countries (Page 19, Lines 330-334).

Reviewer #1, Comment #4: Similarly, the section on Medicaid reimbursement and access might draw on papers that show how reimbursement disparities can directly affect surgical oncology enrollment and availability. Without tying these policy findings back to patient outcomes, the paper risks reading like a pure health economics exercise rather than a piece of cancer policy research.

• Author Response: We agree that our discussion on Medicaid reimbursement was underdeveloped. We have now revised this section to explicitly discuss how lower reimbursement rates could potentially disincentivize provider participation and create access barriers for vulnerable patient populations, centering the discussion more around patient outcomes (Page 16-17, Lines 284-289). However, it must be noted that there are limited studies that specifically look at how rate variations for negotiated rates correspond with patient outcomes, as much of this literature is focused on reimbursement rates. Nonetheless, we have revised the discussion to incorporate this important point, as well as explicitly mentioned the lack of patient data in the limitations (Page 20, Lines 362-363).

Reviewer #1, Comment #5: Another point that needs attention is the interpretation of Certificate of Need and All-Payer Claims Database findings. The current manuscript highlights counterintuitive associations (e.g., CoN laws linked to higher rates), but the discussion does not fully engage with why these paradoxes might occur in the specific context of bone tumor surgery. One way to frame this is through the lens of unintended policy consequences, as transparency laws or market-entry restrictions may actually consolidate market power. Bringing in the broader oncology policy discussion, this would help place these observations in a larger framework of how reforms sometimes fail to achieve their intended cost-control effects.

• Author Response: Thank you for noting this oversimplification. As suggested by the reviewer, we now frame the counterintuitive results for CoN laws (Page 17, Lines 296-300) and APCDs (Page 17, Lines 305-313) through the lens of unintended policy consequences in the context of bone tumor surgery, discussing how these regulations might consolidate market power or lead to price leveling rather than cost reduction in a specialized market like orthopaedic oncology.

Reviewer #1, Comment #6: Lastly, the paper would benefit from more clarity on limitations. It is noted that reconstructive procedures are not captured, but this is a major caveat, because in real-world bone tumor surgery, reconstruction is often more resource-intensive than resection itself. That omission could substantially change the cost profile, and readers need a fuller acknowledgement of this. The writing also occasionally becomes too descriptive, listing results without embedding them into a coherent argument. I encourage the authors to rework the discussion so that each section not only reports the findings but also interprets them in light of cancer care delivery, patient equity, and future policy design.

• Author Response: We thank the reviewer for highlighting these important areas for improvement. We have rewritten the limitations section to give greater emphasis to the exclusion of reconstructive procedures, acknowledging this as a major factor that impacts the total cost profile (Page 19, Lines 345-348). The entire discussion section has also been revised to be more interpretive, guided by both reviewer comments.

Reviewer #2, Comment #1: This manuscript presents a timely and important analysis of price variation in radical bone tumor resections using a large national dataset derived from the Turquoise Health database. The authors investigate negotiated payor rates for CPT codes 24150 and 27365, examining associations with hospital characteristics, payor types, and state-level health policies. The study is well-structured, methodologically sound, and contributes to the literature on healthcare cost transparency and orthopaedic oncology.

Reviewer #2, Comment #2: Strengths 1. Novelty and Relevance: The study addresses a gap in the literature by focusing on specialized oncologic procedures, which are often excluded from broader cost analyses. The use of price transparency data for radical bone tumor resections is novel. 2. Robust Dataset: The analysis includes over 285,000 negotiated rates, providing strong statistical power and generalizability across U.S. hospitals. 3. Methodological Rigor: The use of multivariate linear regression and stratified analyses by CPT code is appropriate and well-executed. The authors control for multiple confounders and present confidence intervals and p-values clearly. 4. Policy Implications: The findings have implications for healthcare policy, particularly regarding the unintended consequences of Certificate of Need laws and price transparency mandates. 5. Clarity and Organization: The manuscript is well-written, with clear tables and logical flow from introduction to discussion.

• Author Response: Thank you for the positive feedback and for noting the strengths of this study. We appreciate the reviewers’ attention to detail and hope that the subsequent changes below help make this manuscript more rigorous for publication.

Reviewer #2, Comment #3: Suggestions/Concerns: 1. Scope of Procedure Pricing:

• Author Response: As noted in Reviewer #1 Comment #6, we have now expanded on this major limitation of our manuscript (Page 19, Lines 345-348) and how we are not capturing the full cost profile of patients undergoing bone tumor resection surgeries. While a sub-analysis of reconstruction codes is an excellent idea, the complexity and variability of these procedures (e.g., allograft, endoprosthesis, custom implants) make it difficult to perform a reliable analysis within the scope of the current study. However, we have noted this as a critical area for future research (Page 19, Lines 350-352). Additionally, to our knowledge, the order of CPT codes should not affect the billing of the procedure. Given that this is a study of negotiated payor rates, and not reimbursement rates, we are unable to comment on how prices change based on the order of the procedure.

Reviewer #2, Comment #4: 2. Data Source Limitations: While the Turquoise Health database is a valuable resource, the potential for incomplete or inconsistent reporting by hospitals should be discussed more thoroughly, especially regarding the reliability of negotiated rates. It would be great if possible to only include those hospitals who actually billed at least one of those procedures. I’d be curious how many of these resections are happening at particularly small hospitals, or if the costs at hospitals who actually perform these operations regularly are higher or lower than those that do not do these procedures. Given this are largely tertiary care type procedures, there may be many hospitals where neither of these CPTs is utilized in a given year, making the negotiated rate rather unimportant. Those hospitals with under 100 beds may not do these procedures, making that hospital less keen to negotiate for a higher rate. I think this limitation needs to be made more clear in the limitations section.

• Author Response: This is an important point that we had not sufficiently addressed. We have added two sentences to our limitations section expanding on this limitation of the Turquoise Health database (Page 19-20, Lines 355-359).

Reviewer #2, Comment #5: Is it possible to evaluate by region using this database? It would be interesting to know whether certain US regions (by US Census subgroups, for example) are significantly different.

• Author Response: We thank the reviewer for this interesting suggestion. We have now added a sub-analysis of changes in rates according to the 9 U.S. Census Bureau divisions. We have updated Table 1 to descriptively list the number of rates in each region, as well as added “Region” into both multivariable regressions (Table 2 and Table 4). Given that the inclusion of another variable shifts all results, we have now updated the correct rates in the new adjusted analysis for both CPT codes throughout the manuscript. We have added a discussion (Page XXX, Lines 259-268). of these findings into the results and appropriately updated all in-text values in the results.

Reviewer #2, Comment #6: 3. Discussion: The discussion mentions that for patients with primary or metastatic disease the lack of price predictability may create uncertainty. For nearly all patients in this condition, the cost of their operation is but a small part of the price of their overall care. I do not think this conclusion is justified by the data presented. You’d have to show that the cost of the resection (and reconstruction) constitute a major part of the total cost of care. I think this sentence should be omitted.

• Author Response: We thank the reviewer for noting this issue. We have removed this sentence from the discussion (Page 14, Lines 234-236).

Reviewer #2, Comment #7: I think the paragraph about higher cost at larger hospitals should reflect that there is no direct data here that I saw suggesting that these procedures actually happen at smaller hospitals. It would be really valuable to have some data to that effect. While this is a potential issue, it may be a small or a non-issue if few of these operations are actually happening at small hospitals. Similarly, it is hard to place context for the findings on critical access hospitals, as we are not at all sure how often these operations happen at critical access hospital. At least in my area, I think any of these patients who might benefit from one of these operations would be transferred to another hospital.

• Author Response: This is an excellent point that ties back to Comment #4. We have revised our discussion of hospital size and type (including CAHs) to be more cautious in our interpretation, acknowledging that negotiated rates from smaller facilities may not reflect actual case volumes (Page 19-20, Lines 355-359).

Reviewer #2, Comment #8: With suggestions incorporated as above, I think this paper could highlight that the negotiated rates are highly variable. Recognizing that these are negotiated rates, which do not necessarily reflect where/if these procedures are happening, would be an improvement. It would also be useful to understand if this degree of variability is similar to or different to the variability for more common procedures. As in: is this a problem throughout US healthcare, or is somehow the problem more of an issue for humerus/femur bone resections.

• Author Response: We have now emphasized one of the main conclusions of the study to be regarding the high-rate variations as examined through the variables included in this study (Page 21, Lines 371-372). We have also contextualized our findings within other articles discussing rate variations (Page 15, Lines 234-238).

Reviewer #3 Comments: On review of this reviewers’ comment, we felt that it was likely meant for another paper. For example, the reviewer makes comments about figures pertaining to signaling pathways, resistance mechanisms for chemotherapy/immunotherapy, and discussions about tumor biology, which are not relevant to this manuscript’s content. Thus, we have not responded to Reviewer #3 concerns, but remain available for additional changes as requested.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0343676.s008.docx^{(25.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0343676.r003

Decision Letter 1

Xiaoen Wei

30 Dec 2025

Dear Dr. Bhat,

Please submit your revised manuscript by Feb 13 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Xiaoen Wei

Academic Editor

PLOS One

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #4: All comments have been addressed

Reviewer #5: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #4: Yes

Reviewer #5: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #4: Yes

Reviewer #5: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #4: Yes

Reviewer #5: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #4: Yes

Reviewer #5: Yes

**********

Reviewer #4: okmanuscript is now suitable for acceptance.

The authors have clearly addressed the major issues raised, and the manuscript is now suitable for acceptance.

Reviewer #5: Sincere gratitude to the authors for the novel manuscript on radical resection. Although the comments earlier raised have been addressed, I still believe some revisions can still be made to make the manuscript a more robust read for the viewers.

General Comments

- The study is mainly focused on orthopaedic surgeries thus the title be revised to reflect it. The present title may confuse readers to think of the study as being generalised to all radical resections in the whole body.

- A list of abbreviated terms should be included in the manuscript.

Abstract

- Write the full iteration of NP on Line 40.

Introduction

- Recast statement as it is too long on Lines 54-57

- While primary bone cancers account for only 0.2% of primary malignancies in the U.S.( this statement should be cited accordingly (Lines 54-55))

- Despite advancements in medical therapies (Line 58); this statement should be cited accordingly

Methods

- Line 103 : Following studies with similar methodologies; the studies should be cited

- Lines 103 - 104 - "we then excluded outliers within the top and bottom 10% of all negotiated rates" ; Was this discussed with your statistician as there are statistical ways to include or exclude outliers?

- Line 117 - Data on NP independent; Write the full meaning of NP as this is its first usage

- Line 127 - Procedure rates are standardized in 2024 U.S. Dollars. ; Kindly clarify 2024 US Dollars.

- Line 129 - What does authors mean by bed range, does it mean the number of available beds in a facility? Authors should be very specific in language utilization.

Statistical Analysis

- Line 148 - Authors should indicate that mean (SD) or median (IQR) will be used as indicated.

- Inferential tests should also be performed across categories to glean reasonable insights into significant differences across meaningful categories.

- Authors should specify the assumptions taken to use multivariate linear regression and the parameters used to determine the variables to be included in the model. Did authors check assumptions like multicollinearity, heteroskedascity and others?

- What were the model performance metrics used to justify the validity of each of the models such as R squared or adjusted R squared?

- All these parameters will determine the validity of the model and the validity of the results.

Results

-Line 166 - Authors should only report the statistically relevant descriptive statistic should it be median (IQR) or mean (SD). Reporting both is unnecessary.

- the results were mostly repetition of the information on the tables and this should not be so

- there was over-utilization of tables; visualisation graphics such as bar plots, box plots and other visualisation means ought to be utilised.

- regression tables can be presented in more explanatory formats with importance placed on models and parameters that explain most of the variance seen in individual models

- Model metrics should be added to the footnotes of all models

References

- There should be consistency in font type, size and line spacing with other sections of the manuscript.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #4: No

Reviewer #5: Yes: Adetayo Aborisade

**********

To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation.

NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

Attachment

Submitted filename: Revised radresection manuscript v4.docx

pone.0343676.s007.docx^{(124.1KB, docx)}

PLoS One. 2026 Feb 26;21(2):e0343676. doi: 10.1371/journal.pone.0343676.r004

Author response to Decision Letter 2

5 Feb 2026

PONE-D-25-44873R1

Exploring the Drivers of Price Variation in Orthopaedic Radical Bone Tumor Resection: A Nationwide Database Study

We sincerely thank the reviewers for both their positive feedback and their suggestions for improvement. Below, we provide our point-by-point response. Please note that the line numbers provided correspond to the “Tracked Changes” version of the manuscript.

Reviewer #4, Comment #1: Manuscript is now suitable for acceptance. The authors have clearly addressed the major issues raised, and the manuscript is now suitable for acceptance.

• Author Response: Thank you for your positive assessment. We hope the changes that have been made in response to Reviewer #5 now make the manuscript suitable for acceptance.

Reviewer #5, Comment #1: Sincere gratitude to the authors for the novel manuscript on radical resection. Although the comments earlier raised have been addressed, I still believe some revisions can still be made to make the manuscript a more robust read for the viewers. The study is mainly focused on orthopaedic surgeries thus the title be revised to reflect it. The present title may confuse readers to think of the study as being generalized to all radical resections in the whole body.

• Author Response: Thank you for the positive overall assessment, and feedback on our title. The title has now been revised to include the term “Orthopaedic.” (Page 1).

Reviewer #5, Comment #2: A list of abbreviated terms should be included in the manuscript.

• Author Response: We have now included a list of abbreviated terms, presented in the order that they appear in our manuscript, following the keywords section (Page 2).

Reviewer #5, Comment #3: Abstract. Write the full iteration of NP on Line 40.

• Author Response: Thank you for noting this omission. We have now spelled out NP at its first mention in the abstract.

Reviewer #5, Comment #4: Introduction. Recast statement as it is too long on Lines 54-57. “While primary bone cancers account for only 0.2% of primary malignancies in the U.S.( this statement should be cited accordingly (Lines 54-55))”

• Author Response: We have now rephrased this sentence to reduce its length. We have also made sure that the citations are provided accordingly.

• Change(s):

o “While primary bone cancers account for only 0.2% of primary malignancies in the United States (U.S.), approximately 5.1% of patients with other malignancies report developing bone metastases.[5, 6] The national cost burden for patients with metastatic bone disease estimated at $12.6 billion dollars in 2007.[7]” (Introduction, Lines 67-69).

Reviewer #5, Comment #5: Despite advancements in medical therapies (Line 58); this statement should be cited accordingly

• Author Response: Thank you for noting this opportunity for improvement. We have now cited a study that discusses advancements in sarcoma systemic therapies.

Reviewer #5, Comment #6: Methods. Line 103 : Following studies with similar methodologies; the studies should be cited

• Author Response: Thank you for noting this opportunity for improvement. We have now cited the studies with “similar methodologies” referenced in this section.

Reviewer #5, Comment #7: Lines 103 - 104 - "we then excluded outliers within the top and bottom 10% of all negotiated rates" ; Was this discussed with your statistician as there are statistical ways to include or exclude outliers?

• Author Response: We appreciate the reviewers’ attention to detail. We confirm that this decision was discussed with our statistician, and used to be consistent with studies with similar methodologies and to reduce the impact of extreme reporting artifacts. In response to Reviewer #5 Comment #6, we have now added the citations for the similar methodologies referenced.

Reviewer #5, Comment #8: Line 117 - Data on NP independent; Write the full meaning of NP as this is its first usage

• Author Response: Thank you for noting this omission. We have now added this abbreviation.

Reviewer #5, Comment #9: Line 127 - Procedure rates are standardized in 2024 U.S. Dollars. ; Kindly clarify 2024 US Dollars.

• Author Response: We have ensured that we clarify this is 2024 United States (U.S.) Dollars. We have now provided adjusted the introduction to spell out “United States” at its first mention.

o Added Text: “While primary bone cancers account for only 0.2% of primary malignancies in the United States (U.S.)…” (Introduction, Line 67)

Reviewer #5, Comment #10: Line 129 - What does authors mean by bed range, does it mean the number of available beds in a facility? Authors should be very specific in language utilization.

• Author Response: Thank you for this comment. We have now provided a definition of total bed range.

• Added Text:

o “Total bed range was used as a proxy for hospital size and refers to the approximate number of inpatient hospital beds.” (Methods, Lines 141-142)

Reviewer #5, Comment #11: Statistical Analysis. Line 148 - Authors should indicate that mean (SD) or median (IQR) will be used as indicated.

• Author Response: We have now clarified the descriptive statistics section of our methods.

• Added Text:

o “First, a descriptive analysis was conducted to summarize cohort characteristics using frequencies and percentages for categorical variables, and means with standard deviation (SD) or medians with interquartile range (IQR) for continuous variables.” (Methods, Lines 162-163)

Reviewer #5, Comment #12: Inferential tests should also be performed across categories to glean reasonable insights into significant differences across meaningful categories.

• Author Response: While we appreciate the reviewer’s suggestion to perform inferential tests across categories in Table 1, we believe our current multivariate linear regression model already addresses this need in a more statistically rigorous manner. Each regression coefficient (“Estimate”) and its associated p-value represent an inferential test comparing that specific category to the reference group while also controlling for confounders (other hospital, payor, and policy-level variables). This approach avoids the increased risk of Type I errors (false positives) associated with multiple independent hypothesis tests and ensures that the differences reported are not due to confounding factors. Additionally, given that the purpose of the study was not to compare negotiated payor rates or characteristics between the two CPT codes, but rather to provide two examples of CPT codes for bone tumor resections and evaluate them separately, we did not perform inferential testing in Table 1. Therefore, Table 1 is intended purely as a descriptive cohort summary.

Reviewer #5, Comment #13: Authors should specify the assumptions taken to use multivariate linear regression and the parameters used to determine the variables to be included in the model. Did authors check assumptions like multicollinearity, heteroskedascity and others?

• Author Response: We thank the reviewer for this comment. We have updated the Statistical Analysis section to explicitly state the assumptions tested for our multivariate linear regression models. Specifically, we confirmed that our data met the requirements for linearity and normality of residuals. To address the reviewer’s concern regarding multicollinearity, we calculated Variance Inflation Factors (VIF) for all predictors, with all values remaining below a threshold of 5, indicating no significant multicollinearity.

Reviewer #5, Comment #14: What were the model performance metrics used to justify the validity of each of the models such as R squared or adjusted R squared? All these parameters will determine the validity of the model and the validity of the results.

Reviewer #5, Comment #17: Model metrics should be added to the footnotes of all models

• Author Response: We have now added R2 to the footnotes of all the regression tables, which range between 0.19-0.24. This range indicates that the predictors in our model have moderate explanatory power, aligning with our discussion of many factors that contribute to variation in negotiated prices for radical bone tumor resections. These values indicate both that while our selected variables, as expected, do not account for most of the variation, they do explain some of the variation. It also supports the complexity of the pricing market as described in our discussion section.

Reviewer #5, Comment #15: Line 166 - Authors should only report the statistically relevant descriptive statistic should it be median (IQR) or mean (SD). Reporting both is unnecessary.

• Author Response: Thank you for this comment. Given the size of our dataset, we have now removed the median (IQR) and focus solely on reporting the means with standard deviations.

Reviewer #5, Comment #16: the results were mostly repetition of the information on the tables and this should not be so.

• Author Response: Thank you for this important point. We have now significantly revised the results to remove numbers alone within the text and focused on synthesizing the data to allow for an easier read of the results section and a more direct answer to the research question. In response to comment #17, we have also replaced the regression tables with Figures and moved all tables to an eAppendix, which we also believe improves the flow and readability of the results. Consistent with guidelines for writing peer-reviewed manuscripts, we have not provided any interpretation of findings.

• Added Text (examples):

o “Compared to hospitals with 1-100 beds, rates mostly increased with bed count and were the highest in facilities with 1500+ beds (+$797.46, p<0.001).” (Results, Lines 206-208)

o “Payor class was also associated with price variation.” (Results, Line 217)

o “All payor classes had significantly lower rates when compared to the Commercial payor class, with the Medicare advantage class having the lowest rates ($-2946.11, p<0.001).” (Results, Lines 270-272)

Reviewer #5, Comment #17: there was over-utilization of tables; visualisation graphics such as bar plots, box plots and other visualisation means ought to be utilised.

- regression tables can be presented in more explanatory formats with importance placed on models and parameters that explain most of the variance seen in individual models

• Author Response: We thank the reviewer for providing this area for improvement. We have now replaced all the in-text regression tables with Figures (Figures 1-8). The Tables have now been moved as eTables to a supplementary document/Appendix.

Reviewer #5, Comment #18: There should be consistency in font type, size and line spacing with other sections of the manuscript.

• Author Response: Thank you for noting this inconsistency. We have now ensured the entire manuscript is 12-point font and double spaced, including the references.

Attachment

Submitted filename: R2_ReviewerResponse_PLOSOne.docx

pone.0343676.s009.docx^{(27.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0343676.r005

Decision Letter 2

Xiaoen Wei

10 Feb 2026

Exploring the Drivers of Price Variation in Orthopaedic Radical Bone Tumor Resection: A Nationwide Database Study

PONE-D-25-44873R2

Dear Dr. Bhat,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support .

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Xiaoen Wei

Academic Editor

PLOS One

Additional Editor Comments (optional):

The authors have satisfactorily addressed the major concerns raised in the previous round of review.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0343676.r006

Acceptance letter

Xiaoen Wei

PONE-D-25-44873R2

PLOS One

Dear Dr. Bhat,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS One. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Xiaoen Wei

Academic Editor

PLOS One

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. State Divisions According to United States Census Bureau.

(DOCX)

pone.0343676.s001.docx^{(20.4KB, docx)}

S2 Table. Multivariable Linear Regression for Payor Rates within the Radical Resection of Humerus Cohort.

(DOCX)

pone.0343676.s002.docx^{(18.6KB, docx)}

S3 Table. Multivariable Linear Regression for Payor Rates within the Radical Resection of Humerus Cohort, Health Policy Sub-Analysis.

(DOCX)

pone.0343676.s003.docx^{(17.2KB, docx)}

S4 Table. Multivariable Linear Regression Results for Payor Rates within the Radical Resection of Femur/Knee Cohort.

(DOCX)

pone.0343676.s004.docx^{(18.6KB, docx)}

S5 Table. Multivariable Linear Regression Results for Radical Resection of Femur/Knee Cohort, Health Policy Sub-Analysis.

(DOCX)

pone.0343676.s005.docx^{(17.2KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0343676.s008.docx^{(25.4KB, docx)}

Attachment

Submitted filename: Revised radresection manuscript v4.docx

pone.0343676.s007.docx^{(124.1KB, docx)}

Attachment

Submitted filename: R2_ReviewerResponse_PLOSOne.docx

pone.0343676.s009.docx^{(27.5KB, docx)}

Data Availability Statement

[pone.0343676.ref001] 1.Bartelstein MK, Boland PJ. Fifty years of bone tumors. J Surg Oncol. 2022;126(5):906–12. doi: 10.1002/jso.27027 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref002] 2.Gazendam A, Ghert M. What’s new in musculoskeletal tumor surgery. J Bone Joint Surg Am. 2023;105(24):1929–36. doi: 10.2106/JBJS.23.00833 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref003] 3.Errani C, Mavrogenis AF, Tsukamoto S. What’s new in musculoskeletal oncology. BMC Musculoskelet Disord. 2021;22(1):704. doi: 10.1186/s12891-021-04590-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref004] 4.Meltzer PS, Helman LJ. New horizons in the treatment of osteosarcoma. N Engl J Med. 2021;385(22):2066–76. doi: 10.1056/NEJMra2103423 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref005] 5.Ryan C, Stoltzfus KC, Horn S, Chen H, Louie AV, Lehrer EJ, et al. Epidemiology of bone metastases. Bone. 2022;158:115783. doi: 10.1016/j.bone.2020.115783 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref006] 6.Franchi A. Epidemiology and classification of bone tumors. Clin Cases Miner Bone Metab. 2012;9(2):92–5. [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref007] 7.Schulman KL, Kohles J. Economic burden of metastatic bone disease in the U.S. Cancer. 2007;109(11):2334–42. doi: 10.1002/cncr.22678 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref008] 8.Morya VK, Magar AG, Park S-H, Noh K-C. Systemic strategies for osteosarcoma: advances and future directions. Discov Oncol. 2025;16(1):1367. doi: 10.1007/s12672-025-02208-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref009] 9.Sonkin D, Thomas A, Teicher BA. Cancer treatments: Past, present, and future. Cancer Genet. 2024;286–287:18–24. doi: 10.1016/j.cancergen.2024.06.002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref010] 10.Bläsius F, Delbrück H, Hildebrand F, Hofmann UK. Surgical treatment of bone sarcoma. Cancers (Basel). 2022;14(11):2694. doi: 10.3390/cancers14112694 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref011] 11.Hayashi K, Tsuchiya H. The role of surgery in the treatment of metastatic bone tumor. Int J Clin Oncol. 2022;27(8):1238–46. doi: 10.1007/s10147-022-02144-6 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref012] 12.Xu M, Wang Z, Yu X-C, Lin J-H, Hu Y-C. Guideline for limb-salvage treatment of osteosarcoma. Orthop Surg. 2020;12(4):1021–9. doi: 10.1111/os.12702 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref013] 13.Kutzer KM, Kiwinda LV, Yang D, Mitchell JK, Luo EJ, Harman EJ, et al. Insurance Payor status and outcomes in pediatric sports-related injuries: A rapid review. Clin Pract. 2025;15(3):52. doi: 10.3390/clinpract15030052 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref014] 14.Kutzer KM, Wu KA, Shenoy DA, Therien AD, Shah SN, Ryan SP, et al. State health care regulations and political affiliation associated with total hip arthroplasty prices across the United States. J Arthroplasty. 2025;40(8S1):S30–5. doi: 10.1016/j.arth.2025.04.007 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref015] 15.Wu KA, Baker RL, Hurley ET, Kutzer KM, Choudhury A, Pean CA, et al. Health care policy and arthroscopic rotator cuff repair prices in the United States. J Shoulder Elbow Surg. 2025;34(1):368–74. doi: 10.1016/j.jse.2024.07.043 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref016] 16.Wu KA, Kutzer KM, Doyle TR, Hurley ET, Pean CA, Anakwenze O, et al. The impact of political partisanship, certificate of need, Medicaid expansion, and area deprivation index on total shoulder arthroplasty prices in the United States. J Shoulder Elbow Surg. 2025;34(1):361–7. doi: 10.1016/j.jse.2024.05.051 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref017] 17.Kasthuri VS, Alsoof D, Balmaceno-Criss M, Daher M, McDonald CL, Diebo BG, et al. Variability in expenses related to spine oncology care: comparison of payer-negotiated rates at National Cancer Institute-Designated Cancer Centers. Spine J. 2024;24(2):304–9. doi: 10.1016/j.spinee.2023.10.008 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref018] 18.Sankaran R, O’Connor J, Nuliyalu U, Diaz A, Nathan H. Payer-negotiated price variation and relationship to surgical outcomes for the most common cancers at NCI-Designated cancer centers. Ann Surg Oncol. 2024;31(7):4339–48. doi: 10.1245/s10434-024-15150-x [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref019] 19.Hospital Price Transparency Rule. https://www.cms.gov/priorities/key-initiatives/hospital-price-transparency. 2021. Accessed 2025 March 7. [Google Scholar]

[pone.0343676.ref020] 20.Shenoy DA, Therien AD, Poehlein E, Zirbes C, Kutzer K, Wu KA, et al. Hospital and regional-level factors associated with negotiated payer rates for hip fracture fixation: A multistate analysis. Value Health. 2026;29(2):206–12. doi: 10.1016/j.jval.2025.09.3051 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref021] 21.Clear Rates Data: Price Transparency Data at Your Fingertips: Turquoise Health; [cited 2024 https://turquoise.health/products/clear_rates_data] [Google Scholar]

[pone.0343676.ref022] 22.Shenoy DA, Parsons KE, Wu KA, Therien AD, Zhang Y, Hayes A, et al. Hospital, regional, and policy-level variations in shoulder arthroplasty prices: A Nationwide database study. JSES Rev Rep Tech. 2025;6(1):100601. doi: 10.1016/j.xrrt.2025.100601 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref023] 23.Kaiser Family Foundation. Status of state action on the Medicaid expansion decision. 2024. https://www.kff.org/affordable-care-act/state-indicator/state-activity-around-expanding-medicaid-under-the-affordable-care-act/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D [Google Scholar]

[pone.0343676.ref024] 24.Certificate of Need State Laws. https://www.ncsl.org/health/certificate-of-need-state-laws. 2024. Accessed 2025 June 29. [Google Scholar]

[pone.0343676.ref025] 25.Hollenbeck BK, Kaufman SR, Oerline M, Modi PK, Caram MEV, Shahinian VB, et al. Effects of Advanced practice providers on single-specialty surgical practice. Ann Surg. 2023;277(1):e40–5. doi: 10.1097/SLA.0000000000004846 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref026] 26.American Association of Nurse Practitioners. State practice environment. https://www.aanp.org/advocacy/state/state-practice-environment. 2024. Accessed 2025. [Google Scholar]

[pone.0343676.ref027] 27.Institute for Health Policy and Practice (IHPP) at the U of NH (UNH). All-Payer Claims Database Legislation by State. National Association of Health Data Organizations (NAHDO). 2024. https://www.apcdcouncil.org/state-efforts/apcd-legislation-state [Google Scholar]

[pone.0343676.ref028] 28.Joynt KE, Harris Y, Orav EJ, Jha AK. Quality of care and patient outcomes in critical access rural hospitals. JAMA. 2011;306(1):45–52. doi: 10.1001/jama.2011.902 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref029] 29.Census Regions and Divisions of the United States. https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf. 2025. Accessed 2025 October 1. [Google Scholar]

[pone.0343676.ref030] 30.White C, Reschovsky JD, Bond AM. Understanding differences between high- and low-price hospitals: Implications for efforts to rein in costs. Health Aff (Millwood). 2014;33(2):324–31. doi: 10.1377/hlthaff.2013.0747 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref031] 31.Beaulieu ND, Chernew ME, McWilliams JM, Landrum MB, Dalton M, Gu AY, et al. Organization and Performance of US Health Systems. JAMA. 2023;329(4):325–35. doi: 10.1001/jama.2022.24032 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref032] 32.Schaefer SL, Ibrahim AM. Overcoming geographic barriers: Surgical care in rural populations. Clin Colon Rectal Surg. 2024;38(1):41–8. doi: 10.1055/s-0044-1786390 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref033] 33.Henderson AP, Moore ML, Holle AM, Haglin JM, Brinkman JC, Van Schuyver PR, et al. Medicaid Reimbursement for Total Hip and Knee Arthroplasty: A State-by-State Analysis Compared With Medicare. J Arthroplasty. 2025;40(2):320–7. doi: 10.1016/j.arth.2024.08.021 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref034] 34.Moore ML, Henderson A, Haglin JM, Brinkman JC, Van Schuyver PR, Bingham JS, et al. Medicaid reimbursement for common orthopaedic trauma procedures is 16.0% less compared with medicare with substantial variability between states. J Orthop Trauma. 2024;38(11):e362–70. doi: 10.1097/BOT.0000000000002901 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref035] 35.Rallo MS, Berke CN, Shaw TC, Bunch JA, Menger RP. State-to-state variation in medicaid reimbursement for neurosurgical procedures: Implications for healthcare access in vulnerable populations. Neurosurgery. 2024;:10.1227/neu.0000000000002823. doi: 10.1227/neu.0000000000002823 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref036] 36.Ejaz A, Spolverato G, Bridges JF, Amini N, Kim Y, Pawlik TM. Choosing a cancer surgeon: Analyzing factors in patient decision making using a best-worst scaling methodology. Ann Surg Oncol. 2014;21(12):3732–8. doi: 10.1245/s10434-014-3819-y [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref037] 37.Voda AI, Bostan I. Public health care financing and the costs of cancer care: A cross-national analysis. Cancers (Basel). 2018;10(4):117. doi: 10.3390/cancers10040117 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref038] 38.Shih Y-CT, Ganz PA, Aberle D, Abernethy A, Bekelman J, Brawley O, et al. Delivering high-quality and affordable care throughout the cancer care continuum. J Clin Oncol. 2013;31(32):4151–7. doi: 10.1200/JCO.2013.51.0651 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0343676.ref039] 39.Niraula S. Strategizing health technology assessment for containment of cancer drug costs in a universal health care system: Case of the pan-Canadian Oncology Drug Review. Cancer. 2019;125(18):3100–3. doi: 10.1002/cncr.32209 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref040] 40.Longo CJ, Fitch MI, Banfield L, Hanly P, Yabroff KR, Sharp L. Financial toxicity associated with a cancer diagnosis in publicly funded healthcare countries: A systematic review. Support Care Cancer. 2020;28(10):4645–65. doi: 10.1007/s00520-020-05620-9 [DOI] [PubMed] [Google Scholar]

[pone.0343676.ref041] 41.Fitch MI, Longo CJ, Chan RJ. Cancer patients’ perspectives on financial burden in a universal healthcare system: Analysis of qualitative data from participants from 20 provincial cancer centers in Canada. Patient Educ Couns. 2021;104(4):903–10. doi: 10.1016/j.pec.2020.08.013 [DOI] [PubMed] [Google Scholar]

PERMALINK

Exploring the drivers of price variation in orthopaedic radical bone tumor resection: A nationwide database study

Devika A Shenoy

William C Cruz

Shamik Bhat

Katelyn Parsons

Aaron D Therien

Kevin A Wu

Christian A Pean

William C Eward

Roles

Abstract

Background

Methods

Results

Discussion

Introduction

Methods

Overview

Inclusion criteria

Data source

Hospital and payor-level variables

State policy variables

Regional variables

Statistical analysis

Ethics approval

Results

Cohort details

Table 1. Overview of Included Negotiated Payor Rates for Radical Resection of Bone Tumors.

Radical resection of humerus cohort

Fig 1. Multivariable linear regression results for the humerus cohort, displayed as a forest plot showing the mean payor rate difference (in USD) for hospital size (”Beds”) and hospital type (“Type”).

Fig 2. Multivariable linear regression results for the humerus cohort, displayed as a forest plot showing the mean payor rate difference (in USD) for “Payor Class.

Fig 3. Multivariable linear regression results for the humerus cohort, displayed as a forest plot showing the mean payor rate difference (in USD) for “U.S Census Bureau region.

Fig 4. Multivariable linear regression results for the humerus cohort, displayed as a forest plot showing the mean payor rate difference (in USD) for various health policies in states that do versus do not (reference) have the policy implemented.

Radical resection of femur/knee cohort

Fig 5. Multivariable linear regression results for the femur/knee cohort, displayed as a forest plot showing the mean payor rate difference (in USD) for hospital size (”Beds”) and hospital type (“Type”).

Fig 6. Multivariable linear regression results for the femur/knee cohort, displayed as a forest plot showing the mean payor rate difference (in USD) for “Payor Class.

Fig 7. Multivariable linear regression results for the femur/knee cohort, displayed as a forest plot showing the mean payor rate difference (in USD) for “U.S Census Bureau region.

Fig 8. Multivariable linear regression results for the femur/knee cohort, displayed as a forest plot showing the mean payor rate difference (in USD) for various health policies in states that do versus do not (reference) have the policy implemented.

Discussion

Conclusions

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Xiaoen Wei

Roles

Author response to Decision Letter 1

Decision Letter 1

Xiaoen Wei

Roles

Author response to Decision Letter 2

Decision Letter 2

Xiaoen Wei

Roles

Acceptance letter

Xiaoen Wei

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases