Incidence of Priapism in Emergency Departments in the United States
Bibliographic record
Abstract
PURPOSE: Priapism is a complex medical emergency that often requires prompt management. In this study, we examine the incidence of this condition in a United States population based setting, and assess patient and emergency department attributes associated with an increased likelihood of hospitalization. MATERIALS AND METHODS: Emergency department visits with a primary diagnosis of priapism between 2006 and 2009 were abstracted from the Nationwide Emergency Department Sample. Univariable and multivariable analyses were performed of patient and hospital characteristics of those admitted with priapism. RESULTS: Between 2006 and 2009 a weighted estimate of 32,462 visits to the emergency department for priapism was recorded in the United States, which represents a national incidence of 5.34 per 100,000 male subjects per year. The incidence of emergency department visits increased by 31.4% during the summer compared to the winter months. Overall 4,320 visits (13.3%) resulted in hospitalization/admission for further management. On multivariable analyses independent predictors of admission included Charlson comorbidity index score 3 or greater (OR 5.67, p <0.001), insurance status (Medicaid vs private OR 1.60, p = 0.001), hospital location (rural vs urban nonteaching OR 0.32, p <0.001), median ZIP code income (very high OR 0.65, p = 0.005), emergency department volume (very high vs very low OR 1.61, p = 0.004), sickle cell disease (OR 2.22, p <0.001) and drug abuse (OR 5.47, p <0.001). CONCLUSIONS: Emergency department visits for priapism are relatively uncommon and occur more frequently during the summer months. The majority of patients are treated and released expediently. Predictors of hospital admission included comorbidity profile, insurance, hospital location and emergency department volume.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".