Supplementary Material for: Efficacy of Mitomycin C Combined with Direct Vision Internal Urethrotomy for Urethral Strictures: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The high recurrence of a urethral stricture after direct vision internal urethrotomy (DVIU) has been a problem for years. Mitomycin C (MMC) is an excellent antifibrosis antigen that has been used in many fields, but its effect on a urethral stricture remains controversial. The purpose of this review was to investigate the effectiveness of MMC in reducing the recurrence rate of a urethral stricture after the first urethrotomy. Methods: Common databases were searched for publications prior to November 30, 2020. Randomized controlled and cohort trials were all included. Recurrence and success rates after the first urethrotomy of the posterior urethra were the main outcomes. Revman 5.3 was used for statistical analysis. Two evaluation systems, the Cochrane risk of bias tool and the Newcastle Ottawa Scale, were used to examine the risk of bias for RCTs and all studies. The quality of evidence was assessed by the Grading of Recommendations, Assessment, Development, and Evaluation standard. Results: Sixteen trials were included, the reporting quality of which was generally poor, and the evidence level was very low to moderate. The addition of MMC could significantly reduce the recurrence rate of urethral strictures (risk ratio [RR] = 0.42; 95% confidence interval [CI]: 0.26, 0.67; p = 0.0002; 9 trials; 550 participants). The results of the subgroup analysis suggested that the effect of MMC combined with DVIU was significant in short (≤2 cm) anterior urethral strictures (RR = 0.39; 95% CI: 0.20, 0.78; p = 0.008), >12-month follow-up (RR = 0.45; 95% CI: 0.26, 0.76; p = 0.003). It also increased the success rate of the first urethrotomy procedure for posterior urethral contracture (RR = 0.74; 95% CI: 0.65, 0.84; p < 0.00001; 7 trials; 342 participants). Low-dose local injection of MMC was the most commonly used method. Conclusion: MMC combined with DVIU is a promising way to reduce the long-term recurrence rate of a short-segment anterior urethral stricture. It also increases the success rate of the first urethrotomy of the posterior urethra. However, more high-quality randomized controlled trials are needed.
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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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.608 | 0.027 |
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".