Abstract
Purpose
To construct a Treatment Decision-making Needs Assessment Scale for cancer patients in China.
Patients and Methods
Referring to the Ottawa Decision Support Framework (ODSF), along with the literature review of domestic and international papers and the existing related scales, we constructed an item pool of the scale. The initial version of the scale was developed as expert correspondence. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed in separate samples. The reliability and validity of the scale were tested by examining 407 cancer patients.
Results
The Treatment Decision-making Needs Assessment Scale for cancer patients comprised three dimensions: Decision-making cognition, Disease knowledge, and Support and resource acquisition, and 15 items. The Cronbach’s α coefficient of the total scale was 0.833, while the test–retest reliability was 0.856. The total content validity index was 0.906. Three factors were extracted using EFA, with a cumulative variance contribution rate of 66.82%. CFA indicated adequate model fit.
Conclusion
The Treatment Decision-making Needs Assessment Scale for Chinese cancer patients demonstrates acceptable reliability and validity, and may be useful for measuring treatment-related decisional needs among Chinese cancer patients in clinical practice and future research.
Keywords: cancer, treatment decision-making needs, assessment scale, reliability, validity
Introduction
The postdiagnosis decision-making of cancer treatment is intricate, often influenced by multiple factors like disease information, family economic status, own preferences, and others’ opinions.1–5 In the Chinese sociocultural context, cancer treatment decisions are often made jointly with family members, and patients may face unique challenges in information access and shared decision-making. Moreover, the ever-evolving assorted treatment programs available for cancer patients are further complicating treatment decision-making.1,6 The selection of a suitable cancer treatment scheme is vital for patients’ survival, quality of life, and satisfaction with medical care.7 Bespoke treatment decisions can decrease patients’ decision-making burden, minimize doctor–patient conflicts, and even enhance patients’ treatment compliance.8–11 Thus, correctly comprehending patients’ needs during treatment decision-making enables the supporting medical staff to provide better treatment decision-making assistance, also serving as a tool to augment the quality of treatment decision-making for cancer patients.
The Ottawa Decision Support Framework (ODSF)12 defines treatment decision needs as decision-makers’ needs in the process of making treatment-related decisions. The demand for treatment decision-making encompasses the decision-makers’ expectations, precise understanding of the treatment plan, and support from others. Arguably, identifying and assessing the treatment decision-making needs of cancer patients marks the first step to constructing decision-making support tools and is imperative in alleviating patients’ decision-making regret. Nevertheless, owing to several reasons, the existing research on treatment decision-making needs of cancer patients largely depends on qualitative research, such as interviews, and some even used two or more statements to ascertain patients’ needs, with the focus mostly on information needs. Thus, few studies are available on cancer patients’ treatment decision-making, understanding of treatment plans, and others’ support.1,13–17
The existing scales on the treatment decision-making needs of cancer patients are fairly simple. For instance, the Ottawa Decision-making Self-efficacy Scale18 measures patients’ ability or belief in treatment decision-making, while Cassileth’s Information Style Questionnaire19 analyzes cancer patients’ information needs, including the dimensions of “Disease and treatment” and “Psychological”. Most existing instruments focus on either information needs or decision self-efficacy, and none provide a comprehensive, integrated assessment of overall decision-making needs. Therefore, a valid and comprehensive scale specifically for Chinese cancer patients remains lacking.
Hence, based on the ODSF theory, this study aims to develop the Chinese version of the Treatment Decision-making Needs Assessment Scale for cancer patients. The development process followed international guidelines for health measurement instruments, such as COSMIN (COnsensus-based Standards for the selection of health Measurement Instruments), to ensure rigor and quality. This scale will assist clinical medical staff in customized treatment decision-making catering to patients’ needs and lifestyles, thereby serving as a basis for better decision-making.
Materials and Methods
Theory
We selected the ODSF, proposed by O’Connor (University of Ottawa, Canada), as the theoretical basis of the scale. The ODSF is a theory that serves for treatment decision-making, encompassing decision demand (core element), decision support, and decision quality. O’Connor and others argue that comprehending the treatment decision-making needs of patients/decision-makers will enable medical staff to offer them targeted treatment decision-making support, thereby reducing their decision-making burden and increasing their satisfaction with treatment plans.12 The theory highlights that treatment decision-making needs comprise personal characteristics, knowledge, expectations, and support of decision-makers. Based on this theory, we defined cancer patients’ treatment decision-making needs as cancer patients’ demands in the process of selecting the treatment plan.
Construction of Item Pool and Scoring Standard
Based on the ODSF, along with China’s national conditions, pertinent domestic and international literature, and related scales, we attempted to construct an item pool of the scale from three aspects—cancer patients’ cognition of treatment decision-making, disease knowledge, and support obtained—and compiled 35 initial items. The scale adopted Likert’s five-point scoring standard (1, very inconsistent; 2, relatively inconsistent; 3, general; 4, relatively consistent; 5, very consistent). The total score of each dimension of the scale is equal to the sum of the scores of each item, and the total score of the scale is equal to the sum of the scores of each dimension. The lower the score, the higher the cancer patients’ treatment decision-making needs and the more medical staff required to provide treatment decision-making guidance.
A total of 25 experts were invited for two rounds of letters, including 22 experts who had worked in the clinical oncology field for >10 years and 3 experts in nursing psychology. The experts’ experience was 14.31±5.71 years; 23 held associate senior and above titles (92.00%); 21 (84.00%) had a master’s degree or above. To prevent memory bias, the two rounds of expert inquiry had an interval of 25 days, and the recovery rate of the inquiry form was 94.20% and 100%, respectively, with an effective recovery rate of 100%. The expert authority coefficients were 0.808 and 0.824, and Kendall’s harmony coefficient test yielded significant results. In this study, we retained items whose average importance was >3.5, the frequency of full marks was >51%, the coefficient of variation was <0.25, and the content validity index (CVI) was >0.78; all the items were screened and revised in conjunction with expert opinions.20 Finally, we deleted 15 items, added 1 item, and revised 4 items, thereby obtaining a total of 21 items.
Study Design and Participants
Using the convenience sampling method, we examined cancer patients in a hospital in Heilongjiang province between November 2023 and August 2024. The inclusion criteria were as follows: (1) patients with a clinically diagnosed cancer; (2) aged ≥18 years; (3) have a certain reading ability; (4) informed consent to participate in this study. The exclusion criteria were as follows: (1) patients with major diseases unrelated to cancer, such as heart failure. The Ethics Review Committee of the First Affiliated Hospital of Harbin Medical University approved this research (No.:2024435).
Data Collection
The data collection was divided into four rounds. The first round tested the scale readability, and 20 cancer patients were selected for the test. The second round involved project analysis. The sample size for the factor analysis was set at 5–10 times the number of items in the scale, and the preliminary version of the scale contained 21 items.21,22 With 10% of potential invalid questionnaires, the target sample size was 116–247 participants; thus, we collected 160 questionnaires, of which 157 valid samples were used for exploratory factor analysis (EFA). The third round focused on EFA and confirmatory factor analysis (CFA); CFA required a sample size exceeding 200.23 Considering the invalid questionnaire rate of 20%, at least 250 participants were needed. From a total of 418 questionnaires collected, 407 were valid, among which 250 were used for CFA. The fourth round focused on the reliability analysis, and the test sample size of retest reliability was 30 participants.
Quality Control
Training group members used unified instructions. The selection of study participants was strictly regulated per the eligibility criteria. The scale was primarily filled by the participants themselves. For participants with low academic qualifications and poor eyesight, the researchers explained the items but the researchers could not alter their original intention. The standard of the invalid scale was set as follows: the general information questionnaire was missing >2 items or all the items were the same.
Practicability
To verify scale practicability, variance analysis was used to test whether patients had varied treatment decision-making needs for different cancer types. We assumed that the higher the education level of cancer patients, the higher the total score of their treatment decision-making needs and the lower their treatment decision-making needs.
Statistical Analysis
All statistical analyses were performed using EpiData 3.1, SPSS 23.0, and AMOS 25. Content validity index (CVI) included item-level CVI (I-CVI) and scale-level CVI (S-CVI). The I-CVI and S-CVI/average (S-CVI/Ave) were calculated based on experts’ ratings using a 4-point scale (1 = irrelevant, 2 = weak correlation, 3 = strong correlation, 4 = very relevant). A CVI value greater than 0.78 was considered indicative of acceptable validity. The CVI >0.78 signifies acceptable validity.20,24
The screening criteria for project analysis were as follows: (1) critical ratio method, the first 27% and the last 27% of the total score of the scale were defined as high grouping and low grouping, respectively. We analyzed the differences between both groupings in item scores and deleted the items with P > 0.05 or t-statistics <3. (2) Coefficient of variation method, coefficient of variation = standard deviation/mean; items with coefficient of variation <0.2 were deleted. (3) Cronbach coefficient method, we deleted items that visibly reduced Cronbach coefficient in the total table (4) Correlation coefficient method, the correlation coefficient between items was >0.8; we deleted items with a correlation coefficient between items and scale <0.4. (5) Factor analysis, items with a factor load <0.5 were deleted.20
Scale validity was tested using structural validity and content validity. The structural validity of the scale was tested using EFA and correlation analysis. The test criteria of EFA were: (1) the extracted eigenvalues were >1; (2) the cumulative variance contribution rate was >50%; (3) the factor load of a single item was >0.5, with no double load; (4) each factor must have at least 3 items; (5) conformed to the principle of gravel map. The test criteria of correlation analysis were: (1) the correlation coefficient between factors was 0.1–0.6; (2) the correlation coefficient between the factor and the total scale was 0.3–0.8.25,26
Notably, CFA tests the ability of the factor structure defined by EFA to fit data when the dimensions of the scale are known.26 Scale reliability was determined by two criteria: (1) Cronbach’s α coefficient >0.7 signifies good internal consistency of the scale;26,27(2) 30 participants were retested after 2 weeks, and the retest reliability coefficient was >0.7, suggesting a stable measurement result of the scale.26,27
Results
Project Analysis
(1) t-Test, a statistical difference was observed between the high and low scores (P < 0.05), and all t-statistics were >3; thus, all items were reserved. (2) Coefficient of variation method, the coefficient of variation of items 1 and 2 was 0.191 and 0.188, respectively, which were <0.2; thus, we proposed to delete them. (3) Correlation coefficient method, the correlation coefficient between items 3 and 8 was >0.8; one of them had to be deleted. (4) Cronbach coefficient method, item 11 was reverse, and Cronbach’s α coefficient of the scale increased from 0.734 to 0.883 after deletion; thus, it had to be deleted. (5) Factor analysis method, the factor loads of items 17 and 21 were 0.477 and 0.495, respectively, which were <0.5; thus, we proposed to delete them. Finally, items 1, 2, 3, 11, 17, and 21 were deleted, totaling 6 items, and 15 items were retained.
Descriptive Results
A total of 407 cancer patients participated in this study. While 157 cases underwent EFA, 250 underwent CFA. The study group’s age was 53.62 ± 10.14 years. Table 1 presents participants’ sociodemographic characteristics: 215 men (52.83%); 160 participants with junior high school education or below (39.31%); 258 (63.39%) never participated in treatment decision-making. Among the decision-making methods, 184 (45.21%) made decisions along with their doctors or families. A total of 162 patients (39.80%) were diagnosed within 1 month. Regarding the disease staging, 160 cases (39.31%) were in the middle stage, and lung cancer accounted for 30.96% of cases (n = 126).
Table 1.
Descriptive Data (N = 407)
| Sociodemographic Variables | N | % | |
|---|---|---|---|
| Gender | Male | 215 | 52.83% |
| Female | 192 | 47.17% | |
| Age | ≤ 30 | 31 | 7.62% |
| 31–45 | 57 | 14.00% | |
| 46–60 | 160 | 39.31% | |
| ≥ 61 | 159 | 39.07% | |
| Marital Status | Single | 91 | 22.36% |
| Married | 316 | 77.64% | |
| Education Status | Junior high school and below | 160 | 39.31% |
| Senior high school | 96 | 23.59% | |
| Technical secondary school or junior college | 85 | 20.88% | |
| Bachelor’s degree or above | 66 | 16.22% | |
| Place of Residence | Village | 146 | 35.87% |
| Cities and towns | 261 | 64.13% | |
| Living Conditions | Live in solitude | 118 | 28.99% |
| Live with your family | 289 | 71.01% | |
| Occupation | Worker | 112 | 27.52% |
| Medical personnel | 19 | 4.67% | |
| Farmer | 106 | 26.04% | |
| Freelance | 46 | 11.30% | |
| Unemployed | 78 | 19.16% | |
| Other | 46 | 11.30% | |
| Personal Monthly Income | 0–2000 | 211 | 51.84% |
| 2001–5000 | 122 | 29.98% | |
| ≥ 5001 | 74 | 18.18% | |
| Types of Cancer | Respiratory system | 126 | 30.96% |
| Urinary system | 86 | 21.13% | |
| Genital system | 89 | 21.87% | |
| Digestive system | 106 | 26.04% | |
Content Validity
The S-CVI of the official version of the scale was 0.906, while the I-CVI of 15 items was 0.781–1.000.
Structural Validity
EFA
Using the principal component analysis (PCA) and maximum variance rotation, the Kaiser–Meyer–Olkin (KMO) value of the scale was 0.879, and Bartlett’s test yielded significant results, demonstrating the scale’s suitability for row factor analysis. Based on the principle of the gravel map test, three factors were extracted, all of which comprised at least three items. Moreover, the cumulative variance contribution rate was 66.82% (Table 2), enabling the attainment of the scale’s official version. Per the content of the item, the three factors were named decision-making cognition, disease knowledge, and support and resource acquisition.
Table 2.
Factor Load of the Items in the Treatment Decision-Making Needs Assessment Scale for Cancer Patients
| Item No. | Items | Decision-Making Cognition | Disease Knowledge | Support and Resource Acquisition |
|---|---|---|---|---|
| 1 | I know the rights and responsibilities of patients in making treatment decisions. | 0.665 | ||
| 2 | I know which treatment scheme is suitable for my family situation. | 0.662 | ||
| 3 | I know which treatment is right for me. | 0.592 | ||
| 4 | I know how to participate in my treatment decision. | 0.574 | ||
| 5 | I understand the occurrence and development of diseases. | 0.865 | ||
| 6 | I understand the duration of treatment for this disease. | 0.815 | ||
| 7 | I understand the approximate cost involved. | 0.798 | ||
| 8 | I know the stages of the disease and the cure rate. | 0.713 | ||
| 9 | I know all the treatments for diseases. | 0.823 | ||
| 10 | I can get full support from my family during my choice of treatment plan. | 0.729 | ||
| 11 | I can cope with the negative emotions brought by illness. | 0.657 | ||
| 12 | I can get psychological support from my family or relatives. | 0.756 | ||
| 13 | I can get financial support from my relatives or friends. | 0.738 | ||
| 14 | I can get information about diseases through electronic devices. | 0.834 | ||
| 15 | I have medical advice from trusted medical staff. | 0.634 |
PCA-based EFA is a data-derived method and does not explicitly model measurement error. Results should be interpreted as exploratory rather than confirmatory.
Correlation Analysis Between Factors and Between Factors and Scale
The correlation coefficient between factors was 0.191–0.473 (P < 0.05), while the correlation coefficient between factors and the total scale was 0.505–0.843 (P < 0.05; Table 3).
Table 3.
Correlation Analysis Between Factors and Between Factors and Scale
| Decision-Making Cognition | Disease Knowledge | Support and Resource Acquisition | Total Score | |
|---|---|---|---|---|
| Decision-making cognition | 1.000 | |||
| Disease knowledge | 0.317** | 1.000 | ||
| Support and resource acquisition | 0.473** | 0.191** | 1.000 | |
| Total score | 0.612** | 0.505** | 0.843** | 1.000 |
Note: **P < 0.001.
CFA
CFA was conducted to verify the factor structure derived from EFA. Model fit indices are presented in Table 4.
Table 4.
Confirmatory Factor Analysis Results (N2 = 250)
| Index | Measured Value | Standard |
|---|---|---|
| χ2/df | 1.914 | 1–3 |
| RMSEA | 0.076 | Ideal value: ≤ 0.08; Acceptable value: ≤ 0.10 |
| CFI | 0.916 | Ideal value: ≥ 0.90; Acceptable value: ≥ 0.85 |
| GFI | 0.863 | Ideal value: ≥ 0.90; Acceptable value: ≥ 0.85 |
| NFI | 0.851 | Ideal value: ≥ 0.90; Acceptable value: ≥ 0.85 |
| IFI | 0.918 | Ideal value: ≥ 0.90; Acceptable value: ≥ 0.85 |
| TLI | 0.897 | Ideal value: ≥ 0.90; Acceptable value: ≥ 0.85 |
| RMR | 0.070 | Ideal value: ≤ 0.08; Acceptable value: ≤ 0.10 |
Reliability Test
Cronbach’s α coefficient of the scale was 0.833, and the three dimensions ranged 0.714–0.829, both of which were higher than the standard value of 0.7, signifying good internal consistency of the scale. We retested 30 participants after 2 weeks, and the coefficient was 0.856, among which three dimensions were 0.817–0.905, all of which were >0.7, suggesting the stability of the scale (Table 5).
Table 5.
Results of the Reliability Analysis
| Project | Cronbach α | Retest Reliability |
|---|---|---|
| Decision-making cognition | 0.714 | 0.832 |
| Disease knowledge | 0.811 | 0.905 |
| Support and resource acquisition | 0.829 | 0.817 |
| Total score | 0.833 | 0.856 |
Scale Practicability
To test scale practicability, we reached a consensus by verifying two hypotheses (Table 6). (1) Assuming different cancer types, patients had varying treatment decision-making needs. The statistical results revealed that cancer patients’ treatment decision-making needs in the urinary system, respiratory system, digestive system, and reproductive system were 61.17±11.56, 55.73 ±11.17, 56.98±12.68, and 58.42±10.49, respectively, and the differences were statistically significant (F = 5.398, P = 0.001). (2) Supposedly, the higher the education level of cancer patients, the higher the total score of their treatment decision-making needs and the lower the degree of their treatment decision-making needs. The scores of treatment decision-making needs of cancer patients in junior high school and below, senior high school, technical secondary school or junior college, and undergraduate and above were 54.41±11.15, 57.30±12.11, 60.25±11.03, and 63.54±10.37, respectively, and the differences were statistically significant (F = 3.117, P = 0.026).
Table 6.
The Impact of Social Demographic Data on the Demand Degree of Treatment Decision-Making
| Project | Group | N | Scale Score | F | P |
|---|---|---|---|---|---|
| Education Status | Junior high school and below | 160 | 54.41 ± 11.15 | 3.117 | 0.026 |
| Senior high school | 96 | 57.30 ± 12.11 | |||
| Technical secondary school or junior college | 85 | 60.25 ± 11.03 | |||
| Bachelor’s degree or above | 66 | 63.54 ± 10.37 | |||
| Types of Cancer | Urinary system | 86 | 61.17 ± 11.56 | 5.398 | 0.001 |
| Respiratory system | 126 | 55.73 ± 11.17 | |||
| Digestive system | 106 | 56.98 ± 12.68 | |||
| Genital system | 89 | 58.42 ± 10.49 |
Discussion
The research scale comprises 15 items, and cancer patients’ treatment decision-making needs were assessed from three dimensions: decision-making cognition, disease knowledge, and support and resource acquisition. The total score of the scale was 15–75; the lower the score, the more needs of cancer patients regarding the decision-making assistance from medical staff.
Scientific Nature of the Scale
The research scale was compiled strictly per the scale development program.20 The ODSF is an evidence-based, practical, and neutral theory, which integrates domestic and international literature and related scales to determine the item pool. During expert inquiry, the effective recovery rate of expert inquiry forms was 100%, which demonstrates that experts pay attention to and support our scale. Besides, the expert authority coefficients were 0.808 and 0.824 (>0.8), which demonstrates a high degree of authority among experts and high evaluation reliability of the scale.20 Through the small-scale test of the items, the language expression of the scale items was more suitable for the participants to fill in, and the average filling time was 7 min. Furthermore, the three dimensions of the final version of the scale obtained by item analysis and reliability and validity test corroborated the theoretical framework.
The dimension of decision-making cognition depicts three aspects: (1) patients’ cognition of the power and responsibility of treatment decision-making; (2) the degree of confirmation of treatment plan selection, that is, the degree of decision-making conflict; (3) the degree of cognition of how to engage in treatment decision-making.
The dimension of disease knowledge depicts patients’ demand for knowledge about the occurrence, development, and treatment of diseases. For example, the stage and prognosis of the disease, treatment time, and tentative treatment cost; all these are factors that influence cancer patients’ treatment decision-making.28
The dimensions of support and resource acquisition depict the internal and external support and resource acquisition needs of patients during treatment decision-making. For instance, “I can cope with the negative emotions brought by diseases” reflects the internal support of patients themselves. Although the support subscale of the Decision Conflict Scale also contains content on whether patients can obtain support from others during decision-making, the content is unclear.29 This scale comprises the economic, psychological, and decision-making suggestions regarding support gained by patients in the dimensions of resource and support acquisition. Besides human support, it also contains whether patients can obtain support through electronic devices.
Scale Practicality
Comprehending cancer patients’ treatment decision-making needs is of utmost significance for constructing decision-making aids and executing decision-making interventions. Existing literature indicates that there are few validated scales assessing treatment decision-making needs among universal cancer patients. The Decisional Conflict Scale (DCS),29 widely used in Western countries, mainly focuses on patients’ uncertainty regarding treatment decisions, but may not provide a comprehensive assessment of overall treatment decision-making needs. The Expectation Scale for Patients’ Participation in Medical Decision-making compiled by Chinese scholars Xu et al30 assesses patients’ needs for information on disease severity, communication with doctors, and decision-making tendency, but does not fully cover patients’ needs for resources like cognition of treatment decision-making and support.31,32 The research questionnaire on cancer patients’ engagement in treatment and nursing decision-making, compiled by Sainio and Lauri,33 primarily measures patients’ access to information in treatment decision-making and the medical staff–patient relationship, comprising five parts and 113 items. This questionnaire was introduced to China by Ma and He,34 but has been rarely applied in recent years, which may be related to the number and complexity of the questionnaire items. The scale proposed in this study may assess cancer patients’ cognition of treatment decision-making, disease knowledge, and demand for support and resources, as well as help to differentiate cancer patients with different cancer types and different education levels. Furthermore, the scale items are simple and easy to understand, which may be useful for medical staff in clinical practice.
Scale Reliability and Validity
Structural validity signifies whether and to what extent the scale aligns with the research framework.20,35 All factor loads of 15 items in our scale were >0.5. Together with the principle of the gravel map test, three factors were extracted, and the cumulative variance contribution rate was 66.82%, which is >50%. Several model fit indices, including Cmin/df value (x2/df), RMSEA, CFI, GFI, NFI, IFI, TLI, and RMR, were used to assess the proposed model’s fit.26 The RMSEA, CFI, IFI, and RMR values suggested an excellent fit. Furthermore, acceptable values of other fit indices supported the goodness of the model fit.
The correlation coefficients among the three dimensions and between the dimensions and the total scale all fulfilled the test criteria of correlation analysis. Content validity denotes the degree to which an item can accurately reflect the measured latent variable, and experts can assess whether the item can reflect the measured variable based on the theoretical basis and familiarity. The S-CVI of the scale was >0.90, which suggests that our scale can adequately reflect the measured variables.20 In addition, Cronbach’s α coefficient of our scale was 0.833, and the three dimensions were 0.714–0.829, both of which were higher than the standard value of 0.7, indicating acceptable internal consistency of the scale.36 Furthermore, 30 cancer patients were retested after 2 weeks, and the coefficient was 0.856, among which three dimensions were 0.817–0.905, all of which were >0.7, indicating acceptable stability of the scale.
Limitations
However, several limitations should be acknowledged. This study used a single-centre sampling strategy, which may limit the generalizability of the results. Participants were from a specific region and cultural background, which may influence their responses and scale scores. As this study represents only preliminary validation of the scale, further verification in larger, multi-centre, and culturally diverse populations is required.
Conclusion
The proposed Treatment Decision-making Needs Assessment Scale for cancer patients in China complies with the development program, has good reliability and validity, and could be used to assess the treatment decision-making needs of cancer patients.
Acknowledgments
We thank all the experts for their help and guidance in the process of instrument development.
Funding Statement
This study was supported by the joint construction project of Henan Medical Science and Technology Research Program [approval number LHGJ20220095].
Abbreviations
CFA, confirmatory factor analysis; EFA, exploratory factor analysis; KMO, Kaiser–Meyer–Olkin; ODSF, Ottawa Decision Support Framework.
Data Sharing Statement
The study data can be obtained from the corresponding author.
Ethics
The procedures of this study were all carried out according to Helsinki Declaration. The Ethics Review Committee of the First Affiliated Hospital of Harbin Medical University approved this research (No.:2024435), and informed consent was obtained from all participants.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors declare no competing interests in this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The study data can be obtained from the corresponding author.