What are the risk factors for erectile dysfunction following penile fracture surgery? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Penile fracture is one of the rare urological emergencies resulting in rupture of the tunica albuginea in the penile corpora cavernosa. Sexual intercourse is known to be the most common aetiology of penile fracture, which usually happens during erection. Immediate surgical intervention is crucial to avoid any complications. Erectile dysfunction is the most feared complication after surgery. This meta-analysis aimed to analyse and determine risk factors of erectile dysfunction among patients who underwent penile fracture surgery. METHODS: Literature searching was conducted in several databases, e.g., Pubmed, Cochrane, ScienceDirect, Google Scholar and DOAJ by applying the Boolean term method. Statistical analyses and risk of bias assessment were calculated through RevMan 5.4.1 and the Newcastle Ottawa Scale (NOS), respectively. Outcomes were presented as odds ratio (OR). RESULTS: A total of 6 studies were included, encompassing 527 patients who were diagnosed with penile fracture and underwent surgery for repairment. Risk factors for post-surgery erectile dysfunction were calculated. Age (OR = 0.19, 95% CI [0.07, 0.52], p=0.001), location of fracture (OR = 0.43, 95% CI [0.22, 0.84], p=0.01), and side of fracture (OR = 0.06, 95% CI [0.02, 0.21], p<0.0001) have significant relations with erectile dysfunction. Whereas aetiology, urethral injury, and timing of presentation have statistically non significant effect on the incidence of erectile dysfunction. CONCLUSIONS: This systematic review and meta-analysis showed that patients over 50 years of age, those with midshaft fracture, and those with bilateral fractures are significantly more likely to have erectile dysfunction following penile fracture surgery.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".