Robotic versus laparoscopic colorectal surgery for patients with obesity: an updated systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Obesity poses significant challenges in colorectal surgery, affecting operative difficulty and postoperative recovery. The choice of minimally invasive approach for this patient population remains a challenge during preoperative planning. This review aims to provide an updated synthesis of studies comparing laparoscopic and robotic approaches for adult patients with obesity undergoing colorectal surgery. Methods MEDLINE, Embase and CENTRAL were searched up to August 2023. Articles were included if they compared laparoscopic and robotic colorectal surgery outcomes in adults with obesity (BMI ≥30 kg/m 2 ). Outcomes included overall postoperative morbidity, conversion to laparotomy, and operative time. Inverse variance random‐effects meta‐analyses were used to pool effect estimates. Results After screening 2187 citations, 10 observational studies were included with 3281 patients with obesity undergoing robotic surgery (mean age: 58.1 years, female: 43.9%) and 11 369 patients with obesity undergoing laparoscopic surgery (mean age: 58 years, female: 53.2%). Robotic surgery resulted in longer operative times (MD 46.71 min, 95% CI 33.50–59.92, p < 0.01, I 2 = 93.79%) with statistically significant reductions in conversions to laparotomy (RR 0.50, 95% CI 0.39–0.65, p < 0.01, I 2 = 67.15%). No significant differences were seen in postoperative morbidity (RR 0.94, 95% CI 0.82–1.08, p = 0.40, I 2 = 36.08%). Conclusion These data suggest that robotic colorectal surgery in patients with obesity may reduce the risk for conversion to laparotomy, but at the expense of increased operative times and with no overt benefits in postoperative outcomes. Further high quality randomized controlled trials assessing the utility of robotic surgery in patients with obesity undergoing colorectal surgery are warranted.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".