Incidence and risk factors of rectovaginal fistula after rectal cancer surgery:a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Rectovaginal fistula (RVF) is a unique complication for women after rectal cancer surgery (RCS). Understanding its incidence and risk factors can help further recognize and prevent the disease. Although there have been studies on the risk factors for RVF after RCS, the results show some heterogeneity and even contradictory conclusions due to differences in trial design, sample size, study population, and treatment methods, etc. However, no relevant meta-analyses have been found to date. This study aims to investigate the incidence and risk factors of RVF after RCS through a meta-analysis. Methods A systematic literature was conducted in PubMed, Embase, Web of Science, and Cochrane Library from the establishment of the database to June 2024. The quality of the literature was evaluated using the Newcastle-Ottawa scale. Meta-analysis was performed using R Studio 4.3.2 software. Results A total of 1 randomized controlled study and 7 non-randomized controlled studies were included, involving 4920 patients, among which 134 cases had RVF. The meta-analysis results showed that the incidence of RVF after RCS was 3.2% (95%CI: 1.9% ∼ 7.8%). Neoadjuvant chemoradiotherapy,tumors in lower position,longer surgery duration and double-stapled technique were risk factors for the occurrence of RVF after RCS. Laparoscopic technique was a protective factor for RVF after RCS. T4 stage,age,diabetes,ASA anesthesia classification, Prior hysterectomy, Combined pelvic organ resection, and vaginectomy had no significant effect on RVF. Conclusion The incidence of RVF after RCS is higher than the general cognition, which should be paid attention to by surgeons. The results of this study may help surgeons identify high-risk inpatients in time, take corresponding measures in advance, and expect to reduce the incidence of the disease.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.003 | 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".