Evaluation of the Efficacy and Safety of Laser versus Cold Knife Urethrotomy in the Management of Patients with Urethral Strictures: A Systematic Review and Meta-Analysis of Randomized Clinical Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Urethral strictures generate great morbidity. Two procedures have been described for their management - laser and cold knife techniques - which are still widely used. We aim to assess the safety and efficacy of laser versus cold knife urethrotomy. MATERIALS AND METHODS: We conducted a systematic search of the literature using MEDLINE, EMBASE, LILACS and Cochrane databases and gray literature. Primary outcomes were urethral stricture recurrence, time-to-recurrence and complication rate. Secondary outcomes were quality of life and maximum urinary flow rate (Qmax). Data analysis was obtained using Review Manager 5.2. RESULTS: Out of 137 publications, 4 articles were included in the meta-analysis. At 3 months, the recurrence rate was similar in both groups (0.55, 95% CI 0.18-1.66), but at 6 and 12 months, it was significantly lower in the laser urethrotomy group (0.39, 95% CI 0.19-0.81 and 0.44, 95% CI 0.26-0.75). The analysis of Qmax at 6 months post-intervention suggested a greater improvement in the laser urethrotomy group. A qualitative analysis showed that complications in both procedures were minor and infrequent. CONCLUSIONS: Laser urethrotomy has a lower recurrence rate at 6 and 12 months compared to cold knife urethrotomy. Complications in both procedures are minor and infrequent. Results should be interpreted cautiously, since they were evaluated only for a short term.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it