Long-term mortality and intestinal obstruction after laparoscopic cholecystectomy: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Long-term outcomes of cholecystectomy are largely unknown though it is a common procedure in general surgery. We aimed to investigate the long-term mortality rate and incidence of intestinal obstruction after laparoscopic cholecystectomy. MATERIALS AND METHODS: This systematic review and meta-analysis was reported according to the PRISMA 2020 and AMSTAR guidelines. A protocol was registered on PROSPERO (CRD42020178906). The databases PubMed, EMBASE, and Cochrane CENTRAL were last searched on February 9, 2022 for original studies on long-term complications with n > 40 and follow-up ≥ 6 months. Outcomes were long-term mortality and incidence of intestinal obstruction, and meta-analyses were conducted. Risk of bias was assessed with Newcastle-Ottawa Scale and Cochrane "Risk of bias"-tool according to study design. RESULTS: We included 41 studies that reported long-term follow-up on 1,000,534 patients. Of these, 38 studies reported on mortality (514,242 patients) that ranged from 0 to 32%. Meta-analysis estimated a long-term mortality rate of 2.0% (95% CI 1.7-2.3%) after laparoscopic cholecystectomy. Five studies including 486,292 patients reported on intestinal obstruction that ranged from 0 to 6%. Meta-analysis estimated a long-term rate of intestinal obstruction of 1.3% (95% CI 0.8-1.8%). CONCLUSION: Long-term mortality after laparoscopic cholecystectomy was 2%. The incidence of long-term intestinal obstruction after laparoscopic cholecystectomy was 1.3%.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| 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 | 0.003 | 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".