A Systematic Review and Meta-Analysis on the Efficacy and Safety of Concomitant Laparoscopic Cholecystectomy and Sleeve Gastrectomy in Patients with Morbid Obesity
Bibliographic record
Abstract
Background/Objectives: Rapid weight loss after bariatric surgery is linked to a higher risk of symptomatic gallstone disease, which could require a cholecystectomy. This meta-analysis aimed to assess the efficacy and safety of conducting laparoscopic cholecystectomy concurrently with sleeve gastrectomy in morbid obese patients. Methods: Scopus, PubMed, ProQuest, Web of Science, and Google Scholar were searched for this review. Quality assessment was conducted using the Newcastle-Ottawa Scale for observational studies and ROB2 for randomized clinical trials. Eight of thirteen studies were high-quality. Pooling for BMI difference and LOS was used to measure efficacy, and the pooled proportion was utilized to assess safety through bleeding, wound infection, and leakage. Heterogeneity was explained via leave-one-out analysis and meta-regression. Results: The pooled standardized mean difference in BMI peri-operation was 3.26 (95% CI: 2.51–4.01, p < 0.01), where the age and initial BMI explained 98% of the heterogeneity through meta-regression. The pooled mean of LOS was 3.18 days (95% CI: 2.23–4.14, I2 = 99%, p < 0.001), where age explained 33.22% of the heterogeneity through meta-regression. The pooled proportion of bleeding was 0.03 (95% CI: 0.02–0.04, I2 = 2%, p = 0.42). The pooled wound infection was 0.04 (95% CI: 0.02–0.08, I2 = 80%, p < 0.001), where age accounted for 99% of the heterogeneity. The pooled biliary/gastric leakage was 0.02 (95% CI: 0.01–0.05, I2 = 0, p = 0.52). Conclusions: These findings indicate that the combined procedures can be executed with an acceptable safety profile. The heterogeneity in outcomes underscores the necessity of personalized patient care, standardized perioperative practices, and continuous research to enhance results and mitigate dangers.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".