The management of penile fracture based on clinical and magnetic resonance imaging findings
Bibliographic record
Abstract
Associate Editor Michael G. Wyllie Editorial Board Ian Eardley, UK Jean Fourcroy, USA Sidney Glina, Brazil Julia Heiman, USA Chris McMahon, Australia Bob Millar, UK Alvaro Morales, Canada Michael Perelman, USA Marcel Waldinger, Netherlands OBJECTIVE To present our experience with repairing penile fracture, based on clinical and magnetic resonance imaging (MRI) findings. PATIENTS AND METHODS Between December 2002 and October 2004, 14 men (19–64 years old) presented to our centre with a penile fracture. Two patients had urethral bleeding. MRI was used before surgery in all patients, and the repair comprised a localized longitudinal penile incision in 13 men. This incision was designed according to the tunical tear site and size already depicted by MRI. One case was managed conservatively, as MRI confirmed an intercavernosal haematoma with no tunical tear. The follow‐up was 4–21 months. RESULTS The tear involved one corpus cavernosum in 11 patients; two were associated with urethral injury. The course after repair was uneventful in all men; the follow‐up showed no erectile dysfunction in any. The patients reported neither pain nor penile curvature during erection. CONCLUSION MRI is a simple and informative investigation for evaluating and documenting a penile fracture, and it improves the management plan.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".