The use of tissue sealant in reducing urethrocutaneous fistula event following hypospadias repair: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: One of the most frequent complications following hypospadias repair is urethrocutaneous fistula (UCF) event. Tissue sealant has been introduced as a means to reduce UCF. However, reports regarding its benefits are varied. Thus, we initiated a systematic review and meta-analysis to investigate its role in reducing UCF following hypospadias repair. Methods: We completed a systematic search through the Embase, MEDLINE, and Scopus databases for studies assessing postoperative complications in hypospadias patients undergoing urethroplasty with and without tissue sealant. Cochrane risk of bias 2 (RoB 2) tool was used to assess the quality of randomized clinical trials (RCTs), while the observational studies were assessed with Newcastle-Ottawa Scale. The primary outcome analyzed in this study was UCF, while secondary outcomes consisted of postoperative complications such as edema, infection, and wound dehiscence that increases the risk of UCF formation, measured using odds ratio (OR) with a 95% confidence interval (CI). Results: Six eligible studies comprising three RCTs and three non-randomized studies were included. Patients undergoing hypospadias repair with tissue sealant had lower UCF events (OR = 3.27; 95% CI 1.92-5.58; p < 0.0001). Likewise other post-operative complications, tissue sealant group had a lower rate of edema (OR = 2.29; 95% CI 1.38-3.78; p = 0.001) and infection (OR = 3.87; 95% CI 1.55-9.70; p = 0.004). The difference in wound dehiscence was insignificant between the groups (OR = 2.08; 95% CI 0.21-20.55; p = 0.53). Conclusion: Tissue sealant can reduce UCF events following hypospadias repair as well as edema and infection that increases the risk of UCF formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.008 | 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".