Comparison of robot-assisted surgery, laparoscopic-assisted surgery, and conventional open surgery for the treatment of gastric cancer： A network meta-analysis
Bibliographic record
Abstract
Abstract Objective To systematically evaluate the predictive efficacy of robotic-assisted distal gastrectomy (RG) with D2 lymphadenectomy. Methods Through PubMed, Embase, Cochrane Library, Ovid, and other databases, the English literature comparing three surgical procedures of conventional open distal gastrectomy, laparoscopic-assisted distal gastrectomy, and robot-assisted distal gastrectomy published from 2015 to December 2021 were collected. The included literature's quality was evaluated using the Newcastle-Ottawa Scale (NOS) and Jadad scale, and Meta-analysis was performed using Review Manager 5.4 and R-Studio software. Results A total of 13,724 patients were included in 29 publications. Meta-analysis showed that among the three had surgical modalities, the robotic-assisted treatment took the longest time in terms of operative time and was most likely to cause postoperative intestinal obstruction, but performed best in terms of length of hospital stay and had the best postoperative results in preventing abdominal infection, bleeding, anastomotic leakage, pneumonia, and overall postoperative complications. Conclusion Robotic-assisted distal gastrectomy is safe and feasible. Patients have an excellent postoperative prognosis, but there are still some problems with the robotic-assisted treatment itself and more aspects to be considered clinically. We expect to improve robotic technology and promote the development of minimally invasive surgery in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".