Efficacy and safety of robotic-assisted laparoscopic cholecystectomy for benign gallbladder disease: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Cholelithiasis is a prevalent benign gallbladder disorder that poses significant public health concerns. Robotic-assisted laparoscopic cholecystectomy (RAC) has recently emerged as an innovative technique designed to improve the precision and efficiency of conventional laparoscopic surgery. This systematic review and meta-analysis seeks to evaluate the comparative safety and effectiveness of RAC in relation to standard laparoscopic cholecystectomy (LC). Materials and Methods: This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (CRD42024588142). Literature searches were performed across PubMed, ScienceDirect, ProQuest, and Wiley databases. Eligible studies included both published and unpublished works that compared RAC with LC. Data synthesis was carried out using Review Manager version 5.4, and methodological quality was appraised through the Newcastle–Ottawa Scale. Results: A total of eight cohort studies met the inclusion criteria. The pooled analysis indicated significant differences in hospital stay and conversion rates between RAC and LC (mean difference = −0.44, 95% confidence interval [CI]: −0.78 to −0.10, P = 0.010; odds ratio = 0.35, 95% CI: 0.18–0.70, P = 0.003). In contrast, no significant differences were observed for length of surgery or postoperative complications (mean difference = 7.73, 95% CI: −0.17 to 15.64, P = 0.006; odds ratio = 0.79, 95% CI: 0.30–2.08, P = 0.63). Conclusions: RAC represents a safe and effective option to LC, with particular advantages for certain patient groups.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.005 | 0.005 |
| 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".