Surgical laser therapy for cryptoglandular anal fistula: Protocol of a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Anal fistula is the natural evolution of perianal abscess and one of the most common perianal diseases for adults. For complex fistula, it is still very challenging for anorectal surgeons to manage. With the introduction of laser technique in surgery, it is becoming more and more widely used for the treatment of cryptoglandular anal fistula. During the past decade, numerous studies have reported the clinical effectiveness and postoperative outcomes of different forms of laser treatment for anal fistula. However, as these studies were varied in terms of baseline characteristics, the evidence for the true clinical effectiveness of laser treatment for anal fistula need further critical appraisal. Therefore, the purpose of this study is to evaluate the outcomes of surgical laser therapy for cryptoglandular anal fistula stratified by laser type and Parks' classification through a synthesis of quantitative and qualitative evidence. METHODS AND ANALYSIS: This study will be carried out with adherence to the Cochrane Handbook. We will search PubMed, Cochrane Library, and Embase until June, 2022 to identify all relevant interventional and observational studies examining the effects of laser therapy on the clinical outcomes for cryptoglandular anal fistula. Data extraction from eligible studies will be performed independently by two unblinded authors using standardized extraction forms. Risk of bias assessment for each study will be conducted using Cochrane tool for randomized controlled trials (RCTs) and the Newcastle-Ottawa scale (NOS) tool for observational studies. The DerSimonian-Laird random-effects model will be used to calculate the pooled estimates. Heterogeneity will be examined by subgroup analysis stratified by laser type and Parks' classification and other study characteristics. Potential publication bias will be assessed by funnel plot symmetrical and Egger's regression tests. CONCLUSIONS: The synthesis of quantitative and qualitative evidence of this systemic review will yield updated and comprehensive evidence of laser treatment on specific outcomes, which can provide anorectal surgeons with high level evidence-based recommendations to improve patient care and clinical outcomes. OSF registration number: DOI 10.17605/OSF.IO/36ADW.
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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 | 0.056 | 0.066 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.023 | 0.027 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.004 |
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".