The effect of post-circumcision mucosal cuff length on premature ejaculation
Bibliographic record
Abstract
INTRODUCTION: We aimed to compare the length of mucosal cuff after circumcision in patients with and without a complaint of premature ejaculation (PE). METHODS: Sexually active patients without erectile dysfunction that presented to the urology polyclinic between March 2018 and June 2018 were included in this multicentered, prospective study. The circumcision age of the patients, the person who performed the procedure (surgeon, non-surgeon), penile length, and dorsal and ventral penile measurements were recorded and compared between patients with and without PE. RESULTS: A total of 208 patients were included in the study. The mean circumcision age of the patients was 5.7±4.2 years, and the mean dorsal and ventral mucosal sizes were 15.02±4.58 mm and 16.31±4.92 mm, respectively. PE was present in 106 of the participants. There was no statistically significant difference between the PE and non-PE groups in terms of the person who performed the procedure (surgeon, non-surgeon). However, patients with PE had statistically significantly longer dorsal and ventral mucosal measurements compared to those without PE (p<0.001). CONCLUSIONS: Our study showed that the dorsal and ventral lengths of mucosal tissue left behind after circumcision are a risk factor for PE. Therefore, special attention should be paid not to leave redundant dorsal and ventral mucosal tissue during this procedure.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".