Conversion to laparotomy during laparoscopic hysterectomy: a meta-analysis of prevalence and key risk factors
Bibliographic record
Abstract
Background This meta-analysis aimed to estimate the prevalence and identify risk factors for conversion to laparotomy during laparoscopic hysterectomy (LH) for both benign and malignant gynecologic conditions. Methods A comprehensive search of PubMed, Embase, and the Cochrane Library was conducted to identify studies published between January 2000 and September 2024. Eligible studies reported the prevalence and risk factors for conversion to laparotomy in patients undergoing LH. Studies were assessed for quality using the Newcastle-Ottawa Scale (NOS), and data were extracted on patient demographics, surgical details, and outcomes. A random-effects model was used to pool prevalence estimates and analyze risk factors. Heterogeneity was assessed using the I2 statistic, and publication bias was evaluated with funnel plots and Egger's test. Results A total of 12 studies, encompassing 12,785 patients, were included. The pooled prevalence of conversion to laparotomy was 6% (95% CI, 5%–7%), with significant heterogeneity (I2 = 91.8%, p < 0.001). Conversion rates were higher in patients with malignant conditions (11%; 95% CI, 9%–14%) compared to benign conditions (5%; 95% CI, 4%–6%). Key risk factors included a history of adhesions (OR, 3.13; 95% CI, 1.91–5.11) and higher BMI (OR, 1.20; 95% CI, 1.08–1.34). Protective factors included surgeon experience (OR, 0.22; 95% CI, 0.08–0.59) and high surgeon volume (OR, 0.57; 95% CI, 0.34–0.94). Conclusions Conversion to laparotomy occurs in approximately 6% of LH cases, particularly in patients with malignancy, a history of adhesions, or higher BMI. Surgeon expertise and case volume may reduce the risk, highlighting the importance of preoperative risk assessment.
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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 | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.074 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".