Traumatic blunt urethral injuries in females: A retrospective study of the National Trauma Data Bank
Bibliographic record
Abstract
INTRODUCTION: Female blunt urethral injury (FBUI) is much less common than in males. Due to this rarity, studies of FBUI are largely confined to smaller case series. This study analyzes circumstances associated with FBUI and its contribution to mortality in greater detail. METHODS: Using the National Trauma Data Bank, we analyzed predictors of FBUI, and tested FBUI as a predictor of mortality. Univariate analysis used Chi-squared for categorical data and T-test for continuous data. Multivariate analysis used multiple logistic regression. RESULTS: A total of 245 (0.021%) of 1 185 904 female blunt trauma patients sustained FBUI vs. 2242 (0.145%) for males (p<0.001). Eighty-seven FBUIs (0.097%) occurred under age 16 vs. 153 (0.016%) in older patients (p<0.001). FBUI was more common with motorcycle (n=14, 0.203%), bicycle (n=11, 0.110%), and automobile vs. pedestrian accidents (n=47, 0.146%) than falls (n=72, 0.011%) or automobile accidents (n=61, 0.029%) (p<0.001). FBUI occurred in 114 (0.011%) patients with Injury Severity Score (ISS) <15 vs. 131 (0.091%) with ISS >15 (p<0.001). Slightly more than half (56.7%) of FBUI occurred with pelvic fractures. Age (odds ratio [OR ] 0.95, p<0.001), injury severity (OR 1.05, p<0.001), auto vs. pedestrian (OR 4.1, p<0.001), motorcycle crashes (OR 6.9, p<0.001), and bicycle crashes (OR 3.9, p<0.001) independently predicted FBUI. A total of 9.4% of FBUI patients died vs. 2.5% without FBUI (p<0.001). The association of FBUI with death was not significant on multivariate analysis. CONCLUSIONS: FBUI is more prevalent in young patients with high-force direct trauma. FBUI is not an independent predictor of mortality, suggesting that it is a marker of severe injury rather than a direct cause of death.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".