Abstract
Background
This study aimed to compare perioperative outcomes, costs, and learning-curve characteristics between a “3 + 2” model combined (The operation is performed by three robotic arms, with two assistants providing assistance). with a “seven-step” standardized robotic surgical procedure and conventional robotic surgery for radical distal gastrectomy in distal gastric cancer, and to evaluate its clinical applicability.
Method
A retrospective cohort study was conducted. A total of 215 patients who underwent robot-assisted radical distal gastrectomy at Gansu Provincial People’s Hospital between May 2018 and October 2023 were included. According to the actual surgical approach used, patients were assigned to either the “3 + 2” model “seven-step” group (n = 107) or the conventional robotic group (n = 108). Perioperative indices, postoperative complications, and surgical costs were compared between the two groups. The learning curve was analyzed using cumulative sum (CUSUM) analysis based on operative time.
Results
Robot-assisted surgery was successfully completed in both groups, with no conversion to open surgery and no perioperative mortality. Compared with the conventional group, the “3 + 2” model “seven-step” group had a shorter operative time (P < 0.05). CUSUM analysis showed that the inflection point occurred at case 16 in the “3 + 2” group and at case 19 in the conventional group, indicating that the “3 + 2” group reached a relatively stable operative-time phase earlier. There were no statistically significant differences between the groups in intraoperative blood loss, postoperative complication rate, or postoperative length of stay (P > 0.05). No statistically significant difference in total surgical costs was observed between the two groups.
Conclusion
The “3 + 2” model “seven-step” standardized robotic radical distal gastrectomy for distal gastric cancer demonstrated perioperative safety comparable to that of conventional robotic surgery, while reducing operative time and achieving an earlier transition to a relatively stable phase on the learning curve. Cost differences should be further evaluated in larger samples and with more detailed cost-item analyses.
Introduction
Gastric cancer (GC) is one of the most common malignancies worldwide and is associated with a substantial mortality burden [1]. For resectable disease, radical resection combined with standardized lymph node dissection remains the mainstay of treatment. With the increasing adoption of minimally invasive concepts, gastric cancer surgery has gradually shifted from open procedures to laparoscopic and robot-assisted approaches. Both laparoscopic and robotic surgery offer advantages in terms of reduced incision-related trauma, improved postoperative pain control, faster recovery, and shorter length of hospital stay, and have become commonly used strategies across multiple centers [2].
Robotic systems have a solid foundation for application in radical gastrectomy. Three-dimensional visualization, enhanced instrument articulation, and tremor filtration help overcome certain limitations of laparoscopy in fine dissection and meticulous anatomical work [3]. Multiple studies have also suggested that robotic surgery is reliable with respect to operative stability and anatomical precision [4]. Radical distal gastrectomy requires lymph node dissection in areas such as the supra- and infra-pyloric regions, the superior border of the pancreas, and around the celiac axis, where the operative space is limited, traction vectors are diverse, and exposure must be continuously adjusted. Port placement and interference among robotic arms can affect surgical-field exposure and instrument movement, while the quality of assistant coordination directly influences the efficiency and safety of lymphadenectomy. Therefore, optimizing port configuration and team collaboration has become a key focus for further improving robotic gastric cancer surgery.
The 2021 Chinese Expert Consensus on Robotic Gastric Cancer Surgery recommends a “W-shaped” or linear five-port trocar configuration [5]. In clinical practice, a setup using four robotic arms plus one assistant port has been widely adopted across many centers [6]. This configuration facilitates maintenance of a stable view and traction; however, common issues include spatial conflicts between robotic arms, restricted assistant maneuverability, and lack of synchronization between instrument delivery and traction—problems that are particularly evident during meticulous lymph node dissection. Addressing these issues requires reproducible solutions regarding port utilization, division of labor among robotic arms, and assistant responsibilities, supported by comparative data.
Our center initiated robotic gastrectomy in June 2017, establishing a relatively stable perioperative management system and operative workflow, and has participated in the development of Chinese guidelines related to robotic gastrectomy [7]. Building on existing experience, our team explored a “3 + 2” model based on the commonly used five-port “W-shaped” layout: three robotic arms are used for intraoperative manipulation, and two surgical assistants participate in cooperation, with the second assistant responsible for controlling the third operating arm. To reduce uncertainty caused by operational variability, the team standardized the process of distal gastrectomy and lymph node dissection into a “seven-step” procedural workflow. This study was designed to compare perioperative outcomes between the conventional five-port “W-shaped” approach and the “3 + 2” model “seven-step” protocol in robot-assisted radical distal gastrectomy for distal gastric cancer, and to evaluate its clinical value through learning-curve analysis, thereby providing evidence for surgical workflow optimization and broader technical adoption.
Materials and methods
Clinical information
This was a retrospective cohort study. A total of 269 consecutive patients who underwent robot-assisted radical distal gastrectomy at Gansu Provincial People’s Hospital between May 2018 and October 2023 were initially included. Case screening was completed based on preoperative examinations, pathology reports, and operative records. Patients were classified according to the actual surgical approach into a “3 + 2” robotic surgery group and a conventional robotic surgery group. All surgical procedures were performed by a single senior surgeon with over a decade of subspecialty experience and a personal series of 600 robotic gastrectomies. To ensure procedural consistency, both the surgical technique and perioperative management strictly adhered to a standardized protocol.grouping was determined from medical records documenting port utilization, allocation of robotic arms, and intraoperative team configuration. To reduce selection bias, all consecutive eligible cases during the study period were included in the analysis. Baseline characteristics were compared between groups to assess comparability.
Inclusion criteria
(1) histopathologically confirmed gastric adenocarcinoma; (2) American Society of Anesthesiologists (ASA) physical status class I–III; (3) R0 resection achieved; (4) complete contrast-enhanced abdominal computed tomography (CT) available within 1 week prior to surgery.
Exclusion criteria
(1) peritoneal dissemination, distant organ metastasis, or clinical stage T4b; (2) prior upper abdominal surgery; (3) severe cardiac, pulmonary, hepatic, or renal dysfunction; (4) emergency surgery; (5) conversion to open surgery; (6) history of other malignancies; (7) receipt of neoadjuvant chemotherapy or radiotherapy.
This study followed the ethical principles of the Declaration of Helsinki and was approved by the Ethics Review Committee of Gansu Provincial People’s Hospital. Written informed consent was obtained from all patients before surgery.
Surgical methods
Patient positioning and port placement
Patients were placed in the supine position with a 30° reverse Trendelenburg tilt and a 15° right-side tilt. Trocar sites were determined according to surface anatomical landmarks; port marking and utilization are shown in Fig. 1a and b. The C port was located 2 cm below the umbilicus and fitted with a 12-mm trocar. The R1 port was placed two fingerbreadths below the costal margin along the left anterior axillary line and fitted with an 8-mm trocar. The R2 port was positioned at the umbilical level on the right midclavicular line and fitted with an 8-mm trocar. The A1 port was located symmetrically on the left side corresponding to R2 and fitted with a 12-mm trocar. The R3/A2 port was placed below the right costal margin between the right anterior axillary line and the midclavicular line and fitted with a 5-mm trocar. The textual description is based on patient surface anatomical landmarks; the schematic is intended to illustrate the relative port distribution and differences in functional allocation.
Fig. 1.
a The conventional surgical approach for establishing trocar sites involves using a 3 + 2 configuration. In contrast, b depicts the 3 + 2 surgical approach for establishing trocar sites. (R1 denotes the primary working port for the first robotic arm; R2 denotes the working port for the second robotic arm; R3 denotes the working port for the third robotic arm; A denotes the assistant’s working port; C denotes the observation port)
Team role allocation in the two surgical modes
In the “3 + 2” group, three robotic arms were used for intraoperative manipulation. The primary surgeon at the console was mainly responsible for the camera and two operating arms, while the third operating arm was controlled by the second assistant at the console. The first assistant at the bedside was responsible for instrument exchange through the assistant port, suction, irrigation, energy device replacement, specimen retrieval, and stapler delivery. In addition to controlling the third arm, the second assistant performed liver suspension, adjusted traction vectors, and maintained exposure of key anatomical planes to ensure a stable operative view for the primary surgeon.
In the conventional group, robot-assisted distal gastrectomy was performed following the standard workflow. Two assistants cooperated at the bedside to provide traction and exposure, suction and irrigation, instrument exchange, stapler cartridge delivery, and specimen handling (Fig. 1a and b). The operative steps and extent of lymphadenectomy in the conventional group followed the Expert Consensus on Robotic Gastric Cancer Surgery (2015 edition) and the Chinese Guidelines for Robotic Gastric Cancer Surgery [8]. In both groups, lymph node dissection was performed according to the D1, D1+, and D2 requirements in the Japanese gastric cancer classification system [9].
Seven-step procedural workflow in the 3 + 2 model
To ensure workflow consistency, distal gastrectomy and lymph node dissection in the “3 + 2” group were recorded as seven consecutive steps. Each step incorporated fixed exposure targets, anatomical landmarks, and traction methods to facilitate reproducibility and training.
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Access establishment and abdominal exploration; completion of liver suspension
After pneumoperitoneum was established, abdominal exploration was performed to assess peritoneal seeding and liver metastasis. The robotic arms were docked from the patient’s head side, and the first operating arm was equipped with an ultrasonic scalpel. The second assistant completed liver suspension at the bedside to create a “V-shaped” traction, maintaining elevation of the left hepatic lobe and exposing the lesser curvature of the stomach and the hepatoduodenal ligament region. The first assistant was responsible for camera cleaning, suction, and supplementary traction when needed.
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Management of the right greater curvature and infrapyloric region (stations 4d and 6)
After mobilization of the right greater curvature, dissection proceeded into the infrapyloric region, with exposure of the superior mesenteric vein required. The right gastroepiploic vein was divided when necessary. The right gastroepiploic artery on the surface of the pancreatic head was identified and managed at its origin, followed by clearance of the associated lymphatic and fatty tissues. The primary surgeon performed meticulous dissection and vascular control, the third arm maintained traction on the greater curvature, and the first assistant provided suction, irrigation, and clip delivery support.
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Suprapyloric dissection and duodenal transection; clearance of the hepatoduodenal ligament region (stations 5, 12a, and 12p)
Dissection proceeded along the gastroduodenal artery into the suprapyloric region, with dissection carried to the common hepatic artery and proper hepatic artery, followed by duodenal transection. After clearance of lymphatic and fatty tissues in the hepatic hilum region, the right gastric artery was managed. As traction directions frequently change in this phase, the third arm stabilized traction on the pylorus and the duodenal stump, while the bedside assistants provided suction and stapler cartridge delivery.
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Suprapancreatic lymphadenectomy (stations 7, 8a, 8p, 9, and 11p)
Dissection advanced along the common hepatic artery toward the celiac axis, including management of the left gastric vein. After lymph node clearance around the proximal splenic artery (station 11p), the left gastric artery was managed. Visualization is often affected by the superior pancreatic border and vascular arcades; therefore, the third arm elevated the gastric body and retracted tissue over the superior pancreatic border, the primary surgeon performed fine dissection around vessels, and the first assistant provided continuous suction and irrigation to reduce smoke and oozing-related obscuration.
-
Lesser curvature dissection (stations 1 and 3)
Dissection entered the lesser curvature from the incisura angularis region and progressed proximally toward the cardia, removing lymphatic and fatty tissues along the lesser curvature. The third arm maintained traction on the lesser curvature to ensure clear visualization of the plane outside the mucosal layer, while the primary surgeon performed dissection and hemostasis.
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Left greater curvature dissection (station 4sb)
The greater omentum was separated from the splenic flexure, relevant vascular structures were identified and managed, and lymphadenectomy of the left greater curvature (station 4sb) was completed. Instrument mobility is relatively restricted in this area and traction points require frequent adjustment; the third arm fixed traction on the gastric body, while the bedside assistants provided suction, irrigation, and additional traction as needed.
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Gastric transection, digestive tract reconstruction, and closure
After gastrectomy, gastrointestinal reconstruction was performed with gastrojejunostomy. The bedside assistants delivered the stapler, assisted with suturing, and checked the anastomosis. After placement of an abdominal drain, the incisions were closed and the operation was completed (see Figs. 2).
Fig. 2.
The following diagram illustrates the seven-step method
Intraoperative and postoperative perioperative management were consistent between the two groups. Postoperative drainage management, criteria for drain removal, and discharge standards were implemented uniformly. All patients received preoperative education based on enhanced recovery after surgery (ERAS) principles, and postoperative analgesia, nutritional support, and early mobilization were carried out using the same protocol to minimize confounding due to perioperative management differences.
Observed indicators
Baseline data included sex, age, body mass index (BMI), tumor location, histological type, and pathological stage (AJCC 8th edition). Perioperative outcomes included operative time, intraoperative blood loss, surgical costs, postoperative length of stay, and postoperative complications. Complications are recommended to be recorded according to a standardized grading system and the distribution across grades should be reported in the results to facilitate interpretation of differences in severe complications.
Cusum learning curve analysis
Cumulative sum (CUSUM) analysis was used to evaluate the learning curve. Cases were ordered by surgical date. Let the operative time of the ith case be 
, and let the mean operative time of all cases be µ. The CUSUM value for the nth case was calculated as:
 |
A scatter plot was generated with case number on the x-axis and CUSUM value on the y-axis, followed by curve fitting. Fitting and statistical processing were performed using SPSS 26.0, and model selection was based on the coefficient of determination (
). Statistical significance was set at 𝑃<0.05. The learning-curve inflection point was defined as the case number at which the CUSUM curve entered a sustained downward trend and became stable, representing a phase of reduced fluctuation in operative time and more consistent performance. If multiple primary surgeons were involved, CUSUM analysis is recommended to be stratified by surgeon to avoid misinterpretation caused by pooled curves.
The following paper will provide a comprehensive overview of the relevant literature.
Statistical analysis
SPSS 26.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analyses. Continuous variables with a normal distribution were expressed as mean ± standard deviation (x̄ ± s), and compared between groups using the independent-samples t test. Non-normally distributed data are recommended to be expressed as median (interquartile range) and analyzed using appropriate nonparametric tests. Categorical variables were expressed as number (percentage) [n (%)] and compared using the chi-square test or Fisher’s exact test, as appropriate. All tests were two-sided, and 𝑃<0.05 was considered statistically significant.
Results
Perioperative baseline characteristics
A total of 215 patients were included in this study: 108 in the conventional robotic surgery group and 107 in the “3 + 2” seven-step standardized surgery group. Comparisons of baseline characteristics between the two groups—including sex, age, body mass index (BMI), tumor location, pathological type, and pTNM stage—showed no statistically significant differences (all 𝑃>0.05), indicating good comparability between groups. The detailed distribution of baseline data is presented in Table 1.
Table 1.
Clinical characteristics of patients
| Variables | “3 + 2”Seven-Step Robot-Assisted Surgery Group (107) | Traditional Robotic Surgery Group (108) | statistic | P |
|---|---|---|---|---|
| Age, ,mean(± SD) | 61.10 ± 10.50 | 60.07 ± 9.18 | 0.77 | 0.44 |
| BMI, mean(± SD) | 22.63 ± 3.35 | 22.92 ± 3.20 | -0.64 | 0.52 |
| Gender, n (%) | 0.91 | 0.34 | ||
| Male | 81(75.00) | 74(69.16) | ||
| Female | 27(25.00) | 33(30.84) | ||
| diabetes, n (%) | 0.054 | 0.82 | ||
| No | 99(91.667) | 99(92.52) | ||
| Yes | 9(8.333) | 8(7.48) | ||
| hypertension, n (%) | 0.001 | 0.98 | ||
| No | 95(87.96) | 94(87.85) | ||
| Yes | 13(12.04) | 13(12.15) | ||
| TNM stage, n(%) | 0.39 | 0.82 | ||
| 1 | 33(30.56) | 31(28.97) | ||
| 2 | 29(26.85) | 26(24.30) | ||
| 3 | 46(42.59) | 50(46.73) | ||
| ASA, n(%) | 2.63 | 0.27 | ||
| 1 | 63(58.33) | 71(66.36) | ||
| 2 | 26(24.07) | 25(23.36) | ||
| 3 | 19(17.59) | 11(10.28) | ||
| pathological type, n (%) | 0.14 | 0.71 | ||
| Non-adenocarcinoma | 4(3.70) | 3(2.80) | ||
| adenocarcinoma | 104(96.30) | 104(97.20) | ||
| Differentiation of tumor, n (%) | 1.35 | 0.25 | ||
| Low and undifferentiated | 52(48.15) | 60(56.08) | ||
| well and medium differentiation | 56(51.85) | 47(43.93) | ||
| lauren type, n (%) | 5.45 | 0.07 | ||
| Intestine type | 76(70.37) | 63(58.88) | ||
| Diffuse type | 11(10.19) | 23(21.50) | ||
| mixed type | 21(19.44) | 21(19.63) | ||
| Tumor size, mean(± SD) | 4.10 ± 2.44 | 4.272 ± 2.575 | -0.50 | 0.62 |
| Alb, mean(± SD) | 37.12 ± 6.42 | 38.057 ± 6.05 | -1.10 | 0.27 |
| HB, mean(± SD) | 120.08 ± 30.23 | 123.24 ± 30.69 | -0.79 | 0.45 |
| WBC, ,mean(± SD) | 7.47 ± 9.50 | 6.769 ± 2.452 | 0.74 | 0.46 |
| PLT, mean(± SD) | 218.35 ± 93.92 | 226.75 ± 83.19 | -0.69 | 0.49 |
Surgical outcomes and perioperative parameters
All patients successfully underwent robot-assisted surgery, with no intraoperative conversions to open surgery and no perioperative deaths recorded. Compared with the conventional robotic surgery group, the “3 + 2” seven-step standardized surgery group had a significantly shorter operative time (𝑃<0.001). No statistically significant differences were observed between the two groups in intraoperative blood loss, surgical costs, or postoperative length of stay (all 𝑃>0.05). Regarding postoperative complications, there were also no statistically significant differences in the overall complication rate or the rate of severe complications. Severe complications were defined as Clavien–Dindo grade ≥ III (all 𝑃>0.05). Detailed perioperative outcomes are shown in Table 2.
Table 2.
Comparison of surgical outcomes between two patient groups
| Variables | “3 + 2”Seven-Step Robot-Assisted Surgery Group (107) | Traditional Robotic Surgery Group (108) | statistic | P |
|---|---|---|---|---|
| Operating time, mean(± SD) | 176.41 ± 32.58 | 235.83 ± 23.32 | -15.31 | < 0.001 |
| Intraoperative blood loss, mean (± SD) | 137.01 ± 104.25 | 137.38 ± 125.19 | -0.02 | 0.98 |
| Surgical costs (ten thousand yuan) | 2.027 ± 1.83 | 2.26 ± 1.16 | -1.11 | 0.27 |
| Postoperative hospital stay(d) | 7(6.00–8.00) | 7(6.00–8.00) | -0.992 | 0.31 |
| Total complications, n(%) | 7(6.48) | 11(10.28) | 1.01 | 0.32 |
| serious complications occurred, n(%) | 5(4.63) | 3(2.80) | 0.50 | 0.48 |
Serious complications ≥ grade 3 on the Clavien-Dindo scale
Learning curve analysis (CUSUM)
CUSUM learning curves were constructed using operative time as the indicator, followed by polynomial fitting. For the “3 + 2” seven-step standardized surgery group, the fitted equation was:
CUSUM = 3.4701 × 10⁻⁹n⁶ − 1.4830 × 10⁻⁸n⁵ − 0.0024 × 10⁻⁵n⁴ + … + 38.3215n − 37.3736 (R² = 0.9902). The inflection point of the fitted curve occurred at case 16, and cases 1–16 were considered the technical acquisition phase. For the conventional robotic surgery group, the fitted equation was: 3.8215 × 10⁻⁹n⁶ − 1.5646 × 10⁻⁸n⁵ − 2.7336 × 10⁻⁵n⁴ + … + 27.6973n − 7.7017 (R² = 0.9944). The inflection point occurred at case 19, and cases 1–19 were considered the technical acquisition phase. The CUSUM curves and fitted results are shown the Fig. 3.
Fig. 3.
Learning curve; a The following study will examine the ‘3 + 2’ model in conjunction with the ‘Seven-Step Method’ in the context of programmed robotic-assisted distal gastrectomy, with the aim of producing a learning curve analysis chart. b The following article presents a Learning Curve Analysis Chart for Traditional Robotic Distal Gastric Cancer Radical Resection
Discussion
A substantial body of evidence supports the view that surgical intervention remains the primary treatment for gastric cancer [10]. The minimally invasive paradigm has driven broader adoption of laparoscopic and robotic approaches, and distal gastrectomy performed under minimally invasive conditions can achieve favorable perioperative outcomes. The main technical challenges lie in multi-station lymph node dissection and meticulous dissection around major vessels. Because the operative space is limited and traction vectors change frequently, instrument interference and unstable traction can increase procedural difficulty. Standardized and protocolized workflows are considered beneficial for improving procedural consistency and may also shorten the learning process [11]. After introducing the da Vinci system in June 2017, our center gradually established a relatively fixed port configuration and team-coordination pattern. Based on the commonly used five-port layout, we developed the “3 + 2” model and organized distal gastrectomy into a “seven-step” procedural workflow to reduce uncertainty arising from variations in operative steps.
With respect to perioperative outcomes, no statistically significant differences were observed between the two groups in intraoperative blood loss, postoperative complication rate, severe complication rate (Clavien–Dindo grade ≥ III), or postoperative length of stay (all 𝑃 > 0.05). In the “3 + 2” group, severe complications occurred in 5 patients (4.63%), all presenting as intra-abdominal infections derived from anastomotic leakage (AL). These patients were successfully managed via percutaneous drainage or re-operation. As AL is highly correlated with patient-specific factors and local anatomy, and considering that all such complications were handled by experienced surgeons with no intergroup disparity in incidence, it can be inferred that these occurrences were incidental and not directly influenced by the implementation of the specific intervention mode. These findings suggest that the “3 + 2” seven-step standardized approach did not demonstrate any disadvantage in perioperative safety. Oncologic radicality was constrained by the inclusion criterion of R0 resection; however, oncologic stability and generalizability still require further evaluation using the number of retrieved lymph nodes and long-term follow-up outcomes.
The most prominent finding of this study was the difference in operative time: the “3 + 2” seven-step standardized group had a shorter operative time than the conventional group (𝑃 < 0.001). This difference may be related to the method of exposure, instrument occupancy, and the mode of team coordination. When the third operating arm was controlled by the second assistant at the console, traction and exposure could be maintained more consistently during key anatomical phases. This may have reduced idle time for traction adjustment while the primary surgeon’s two operating arms performed perivascular dissection and lymphoadipose clearance. Meanwhile, the bedside first assistant’s tasks were more focused on suction, irrigation, instrument delivery, and stapler assistance, reducing conflicts that can arise when bedside staff must simultaneously provide traction and deliver instruments. The seven-step protocol organizes dissection in a fixed sequence, making exposure targets and anatomical landmarks for each step more explicit and transitions between steps more predictable, thereby decreasing intraoperative pauses and repeated verification. When port placement and instrument motion ranges are relatively fixed, external robotic arm collisions and instrument crossovers may also be easier to avoid. Together, these factors may explain the observed reduction in operative time.
The Cumulative Sum (CUSUM) method was employed to evaluate the surgical learning curves in this study [12, 13]. Our analysis revealed that the switching point (inflection point) occurred at the 16th case in the “3 + 2” seven-step standardized group, whereas it appeared at the 19th case in the conventional group. These findings suggest that implementing a standardized workflow and optimizing team role allocation allow operative times to reach a plateau phase more rapidly. Consistent with previous literature, standardized surgical pathways have been shown to reduce procedural variability and enhance technical reproducibility [14]. Given that CUSUM analysis is highly sensitive to operator composition and case sequence, a single experienced surgeon performed all procedures to minimize inter-operator heterogeneity. Furthermore, to address potential time-dependent bias, both surgical approaches were conducted during the same period. This study design effectively controlled for confounding factors such as cumulative perioperative management experience, equipment upgrades, and improvements in team synergy.
The role allocation within the ‘3 + 2’ model offers significant pedagogical advantages by decentralizing complex tasks into specialized functions. By managing the third robotic arm at the console, the second assistant gains early exposure to immersive visualization and micro-anatomical coordination—skills traditionally reserved for the lead surgeon. Simultaneously, the first assistant orchestrates bedside maneuvers and intraoperative mentoring, thereby establishing a structured professional development pathway. However, the implementation of this intensified staffing model must be balanced against pragmatic perioperative resource management; specifically, training hierarchies must be calibrated against institutional manpower capacity. Beyond logistical considerations, this study underscores that the observed gains in procedural efficiency are independently driven by team structural optimization rather than technological advancement alone.
Cost remains an important factor limiting wider adoption of robotic surgery [15, 16]. In this study, there was no statistically significant difference in total costs between the two groups (𝑃 = 0.27), although the standardized group showed a trend toward lower mean costs. At present, the data can only suggest a direction of cost change and should not be framed as a definitive “reduction.” The “3 + 2” model uses one fewer robotic arm-related instrument set, which could theoretically reduce certain consumable expenses [17, 18], and shorter operative time may also improve operating-room resource utilization.
This study has several limitations. Its retrospective design and single-center data may introduce selection bias, and unmeasured confounding cannot be fully controlled. The sample size remains limited, and follow-up outcomes were mainly perioperative, with insufficient assessment of long-term oncologic outcomes and quality of life. If the two approaches were implemented during different phases, time-related bias could influence operative time and learning-curve results. Future studies could adopt a multicenter prospective design, clearly define grouping criteria, report surgeon experience and case allocation, provide a complete description of the conventional workflow, offer more granular complication data and cost-component analyses, and incorporate long-term follow-up results to validate generalizability.
Conclusion
This study compared the perioperative performance of the “3 + 2” model combined with a “seven-step” standardized protocol versus the conventional robotic approach in robot-assisted radical distal gastrectomy for distal gastric cancer. There were no statistically significant differences between groups in intraoperative blood loss, postoperative length of stay, overall complication rate, or severe complication rate, whereas operative time was shorter in the “3 + 2” seven-step standardized group Learning-curve analysis indicated that the “3 + 2” seven-step standardized group reached a relatively stable operative-time phase earlier (inflection point at case 16), compared with case 19 in the conventional group. Regarding costs, there was no statistically significant difference in total costs, although the standardized group showed a trend toward lower mean costs; this finding requires further validation using larger samples and itemized cost analyses. The “seven-step” protocol standardizes operative sequence, exposure targets, and anatomical landmarks, while the “3 + 2” role allocation clarifies responsibilities for traction/exposure and bedside instrument delivery, which may improve procedural consistency and facilitate team training and broader adoption. Future work should incorporate prospective multicenter data, long-term oncologic outcomes, more detailed complication profiles and management, and standardized health economic methods to more comprehensively evaluate the clinical value of this approach.
Acknowledgments
The authors thank the patient and her family for providing informed consent for publication.
IRB approval disclosure
This study was approved by the Research Ethics Committee of Gansu Provincial People’s Hospital and was conducted in accordance with the ethical standards set forth in the Declaration of Helsinki (1975).
Disclosures
Yuqi MA, Ade SU, Xinran BI, Zhiheng WU, Juanjuan SUN, Jiguang YIN, Hui CAI, Ming HU, Wentao Zhang, YunTao MA have no conflicts of interest or financial ties to disclose.
Authors' contributions
Literature search and design for Yuqi Ma, Ade SU, and Xinran BI; data collection and analysis for Yuqi Ma, Zhiheng WU, Juanjuan SUN, and Jiguang YIN; and discussion and analysis of hypotheses for Hui CAI, Ming HU, Wentao Zhang, and Yuntao Ma.
Funding
This research was funded by the project titled 'Development of a 3 +2+ X (24JRRA885) Gastric Cancer Surgery Application System Based on a 5G Remote Robotic Platform' and the Lanzhou Science and Technology Program (2022-3-4).
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yuqi Ma and Ade Su contributed equally to this work.
Hui Cai and YunTao Ma contributed equally to this work.
Contributor Information
Hui Cai, Email: caialonteam@163.com.
YunTao Ma, Email: 3575515665@qq.com.
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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 datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.