Fractured Penis: Diagnosis and Management (CME)
Bibliographic record
Abstract
INTRODUCTION: Penile fracture is a well-recognized clinical entity. It is relatively uncommon and is considered a urological emergency. Its management has been a subject of controversy. AIM: In this article, we will review contemporary knowledge of the epidemiology, pathophysiology, evaluation, and evolving management strategies of penile fracture. METHODS: A case report was discussed followed by an English-language Medline review. MAIN OUTCOME MEASURE: Review of the available literature to establish best-practice management. RESULTS: The injury is defined as the traumatic rupture of the corpus cavernosum secondary to a blunt trauma of the erect penis. The condition is underreported. The commonest causes were coital injuries and penile manipulation. The diagnosis was usually fairly straightforward because of the stereotypical clinical presentation. Associated injuries included urethral rupture. Imaging was helpful in selected cases. Conservative measures were associated with increased complications. Most authors advocated early surgical repair. False explorations have been reported. CONCLUSIONS: Penile fracture is a clinical diagnosis. The ideal management has evolved and remains largely surgical. Preoperative imaging should not delay surgical repair.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".