Robotic versus laparoscopic revisional bariatric surgeries: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose: In recent years, the need for revisional bariatric surgery (RBS) procedures has experienced a noteworthy surge to confront complexities and weight recidivism.Despite being a subject of controversy for many, the utilization of the Da Vinci robotic platform (Intuitive Surgical, Inc.) may present benefits in RBS.This study aimed to evaluate the outcomes of robotic RBS in comparison to Laparoscopic RBS.Methods: A meticulous and thorough analysis was ensured through a comprehensive exploration of the literature, which included PubMed, Medline, Scopus, and Cochrane.This exploration was conducted in adherence to the directives outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.The Newcastle-Ottawa scale was used for quality assessment.Results: A total of 11 studies were included in this meta-analysis, comprising 55,889 in the laparoscopic group and 5,809 in the robotic group.No significant differences were observed in the leak, bleeding, operative time, or length of stay across both groups.However, the robotic group showed higher rates of conversion to open surgery (odds ratio [OR], 0.65; 95% confidence interval [CI], 0.53-0.79;p < 0.0001; I 2 = 0%), reoperation (OR, 0.70; 95% CI, 0.57-0.87;p = 0.0009; I 2 = 6%), and readmission (higher rate of readmission in the robotic group; OR, 0.76; 95% CI, 0.62-0.92;p = 0.005; I 2 = 30%) Conclusion: Robotic-assisted bariatric surgery has no significant advantage over conventional laparoscopic surgery.Further research is warranted to explore and evaluate surgeons' methodology and proficiency differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".