Robotic versus laparoscopic left colectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study aimed to review the new evidence to understand whether the robotic approach could find some clear indication also in left colectomy. METHODS: A systematic review of studies published from 2004 to 2022 in the Web of Science, PubMed, and Scopus databases and comparing laparoscopic (LLC) and robotic left colectomy (RLC) was performed. All comparative studies evaluating robotic left colectomy (RLC) versus laparoscopic (LLC) left colectomy with at least 20 patients in the robotic arm were included. Abstract, editorials, and reviews were excluded. The Newcastle-Ottawa Scale for cohort studies was used to assess the methodological quality. The random-effect model was used to calculate pooled effect estimates. RESULTS: Among the 139 articles identified, 11 were eligible, with a total of 52,589 patients (RLC, n = 13,506 versus LLC, n = 39,083). The rate of conversion to open surgery was lower for robotic procedures (RR 0.5, 0.5-0.6; p < 0.001). Operative time was longer for the robotic procedures in the pooled analysis (WMD 39.1, 17.3-60.9, p = 0.002). Overall complications (RR 0.9, 0.8-0.9, p < 0.001), anastomotic leaks (RR 0.7, 0.7-0.8; p < 0.001), and superficial wound infection (RR 3.1, 2.8-3.4; p < 0.001) were less common after RLC. There were no significant differences in mortality (RR 1.1; 0.8-1.6, p = 0.124). There were no differences between RLC and LLC with regards to postoperative variables in the subgroup analysis on malignancies. CONCLUSIONS: Robotic left colectomy requires less conversion to open surgery than the standard laparoscopic approach. Postoperative morbidity rates seemed to be lower during RLC, but this was not confirmed in the procedures performed for malignancies.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".