Smoking is an independent risk factor for stricture recurrence after the urethroplasty: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To clarify the association between smoking and stricture recurrence after urethroplasty. MATERIALS AND METHODS: Pubmed, Web of Science, Embase, and Cochrane databases were searched with keywords: "urethroplasty," "buccal mucosa graft urethroplasty," "oral mucosa graft urethroplasty," "excision and primary anastomosis urethroplasty," "urethral stricture recurrence" until July 1, 2022. Inclusion and exclusion criteria were based on PICOS principles. The quality of included studies was assessed by Newcastle-Ottawa Scale (N.O.S.) system. Hazard ratio (H.R.), odds ratio (OR), and relative risk (RR) with 95% confidence interval (CI) were extracted or re-calculated from included studies. Meta-analysis was performed with Stata 15.0 based on univariate and multivariate data separately. Sensitivity analysis was performed to test the stability of the meta-analysis. I2 was calculated to evaluate heterogeneity. Publication biases were assessed by Egger's and Begg's tests. Funnel plots of univariate analysis and multivariate analysis were also offered. RESULTS: Twenty one studies with 6791 patients were involved in this meta-analysis. The analysis results of the two stages were consistent. In the univariate meta-analysis stage, 18 studies with 5811 patients were pooled, and the result indicated that smoking might promote stricture recurrence (RR=1.32, P=0.001). Based on the adjusted estimate, 11 studies with 3176 patients were pooled in the multivariate meta-analysis stage, and the result indicated that smoking might promote stricture recurrence (RR=1.35, P=0.049). There was no significant heterogeneity in both the univariate and multivariate stages. CONCLUSION: Our study demonstrates that smoking may prompt stricture recurrence after the urethroplasty. Quitting smoking may be a good option for patients undergoing urethroplasty surgery.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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) | 0.001 | 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 teacher head, 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".