Risk factors for delayed wound healing after anal fistula surgery: Protocol of a meta-analytic study
Bibliographic record
Abstract
INTRODUCTION: Delayed wound healing (DWH) following anal fistula surgery is a common complication that prolongs recovery, increases patient morbidity, and imposes significant healthcare costs. Potential risk factors such as diabetes, smoking, fistula complexity, and surgical techniques have been suggested in individual studies, yet no comprehensive synthesis exists to guide clinical practice. This study aims to identify and evaluate risk factors associated with DWH after anal fistula surgery by combining existing evidence and grading the evidence. METHODS AND ANALYSIS: This study will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. We will search PubMed, Embase, Cochrane Library, Web of Science, and grey literature databases from inception to March 2025, with no language restrictions. Observational studies (cohort and case-control) reporting risk factors for DWH, defined as incomplete healing beyond 6-12 weeks post-surgery, will be included. Two independent reviewers will screen titles/abstracts, perform full-text reviews, extract data regarding study design, sample size, risk factors and outcomes, and assess risk of bias using the Newcastle-Ottawa Scale (NOS). Primary outcomes will include odds ratios (OR) or relative risks (RR) for factors such as comorbidities, lifestyle factors, and operative approaches. A random-effects meta-analysis will pool effect estimates if heterogeneity (I² < 50%) permits; otherwise, a narrative synthesis will be conducted. Subgroup analyses will explore differences by study design and patient characteristics, with publication bias assessed using funnel plots and Egger's test. The certainty of evidence will be evaluated with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. DISCUSSION: This study's strengths include its comprehensive search strategy and rigorous methodology, providing a robust synthesis of risk factors. Clinically, identifying modifiable risk factors could enhance preoperative optimization and postoperative care, reducing delayed healing rates. Future studies should standardize definitions of delayed healing and explore under-investigated factors like wound care techniques or microbiome influences to refine risk prediction models. REGISTRATION: PROSPERO CRD420251013602.
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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 | 0.093 | 0.128 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.005 |
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".