Clips closure versus endoloop ligation in laparoscopic appendectomy: a systematic review and meta-analysis of comparative studies
Bibliographic record
Abstract
Introduction: Appendiceal stump closure (ASC) is a key step in performing laparoscopic appendicectomy. Currently, there is no gold standard method to achieve this goal. The ideal method should be safe, easily available, and have a short learning curve. Out of all those appendiceal stump closure methods, the use of hem-o-Lok demonstrates its feasibility in replacing the traditionally used endoloop. In this systematic review and meta-analysis, the authors aim to review the currently available evidence addressing the topic of interest. Method: The PubMed and Embase databases were searched with the paired search terms appendicitis, clip, and endoloop by two authors separately. The quality of the randomized controlled trials was assessed with the Cochrane risk of bias tool, and the quality of the observational studies was assessed with the Newcastle-Ottawa scale. Meta-analysis was conducted with Cochrane Review Manager version 5.4. Result: Eighteen studies were included for quantitative analysis. The appendiceal stump closure time was shortened by 2 min 7 s using a hem-o-lok with 95% CI 1 min 48 s–2 min 26 s, p less than 0.00001. The pooled results of 6 randomized controlled trials demonstrated a statistically significant reduction in operative time of 5.15 min from adopting the hem-o-lok approach ( p =0.001, 95% CI −2.05 to −8.24 min). Both endoloop and hem-o-lok demonstrated a comparable postoperative hospital stay and infective complication profile. Conclusion: The application of Hem-o-Lok demonstrates a comparable to endoloop ligation in terms of operative time and a potential benefit on the complication. When considering financial and technical aspects, it serves as an alternative to endoloop.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.004 | 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".