Complications in simultaneous laparoscopic cholecystectomy and laparoscopic hernia repair: A meta-analysis
Bibliographic record
Abstract
Background Surgeons concurrently perform laparoscopic cholecystectomies and hernia repairs. However, the potential complications of combining these procedures remain uncertain. Methods We conducted a meta-analysis of observational studies following PRISMA guidelines. Utilizing random effect models, we assessed short- and long-term complications associated with simultaneous procedures. We included studies if they reported the sample size, data on the number of events, prevalence of mesh infection, surgical site infection, or any other complications reported by the authors. Both observational and experimental studies were identified through the PubMed, Embase, and Scopus databases. Heterogeneity was quantified using I 2 statistics, and R was employed for analysis. We assessed the risk of bias using the Cochrane risk of bias tool for randomized controlled trial (RCT), New Ottawa scale for observational studies, and JBI tool for case series. Results Ten eligible records (n = 598 participants) were identified. Negligible rates of mesh or surgical site infections were observed. The pooled rates of seroma, recurrent hernia, and other complications were remarkably low (0.04 %, 0.08 %, and 0.03 %, respectively). Overall, the risk associated with simultaneous procedures was minimal. The quality of the included studies was good with some concerns for RCT and the average quality score for observational studies was 6.5. Discussion Simultaneous laparoscopic cholecystectomy and hernia repair appear safe, with very low complication rates. This evidence supports the feasibility of combining these surgeries with minimal risk. However, owing to limited data on this topic, a few studies were included and majority of the studies were not randomized controlled trials; therefore, the issue of unmeasured confounding factors and the lack of a control group should be considered while interpreting the study findings.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.065 |
| 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) | 0.002 | 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".