Robotic gastrointestinal surgery using the Weigao surgical robot system: a single-center prospective analysis
Bibliographic record
Abstract
BACKGROUND: The Weigao surgical robot system (WG-NST600S) was successfully developed in China. In this study, we presented our single-center experience and the short-term outcomes of gastrointestinal surgeries performed using the WG-NST600S system. MATERIALS AND METHODS: Between April 2024 and February 2025, consecutive gastrointestinal surgeries were performed using the WG-NST600S system at the participating institution. Clinical characteristics, as well as surgical and short-term postoperative outcomes, were prospectively collected and analyzed. RESULTS: One hundred and six patients were enrolled in the study. Of these, 9 underwent radical gastrectomy, 16 partial gastrectomy, 58 radical resections for rectal cancer, 7 sigmoidectomy, 5 left hemicolectomy, and 11 right hemicolectomy. The median operative times were as follows: 278 min for radical gastrectomy, 137 min for partial gastrectomy, 245.5 min for radical resection of rectal cancer, 242 min for sigmoidectomy, 290 min for left hemicolectomy, and 311 min for right hemicolectomy. The blood loss was 20 mL for radical gastrectomy, 10 mL for partial gastrectomy, 20 mL for radical resection of rectal cancer, 20 mL for sigmoidectomy, 25 mL for left hemicolectomy, and 20 mL for right hemicolectomy. Postoperative complications were 22.22% for radical gastrectomy, 6.25% for partial gastrectomy, 18.97% for radical resection of rectal cancer, 42.86% for sigmoidectomy, 0% for left hemicolectomy, and 27.27% for right hemicolectomy. All procedures were successfully completed without conversion to open surgery or other unplanned interventions. CONCLUSION: Based on our single-center experience, the WG-NST600S system is a feasible, safe, and effective option for most gastrointestinal surgeries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".