Sex-disaggregated analysis of the injury patterns, outcome data and trapped status of major trauma patients injured in motor vehicle collisions: a prespecified analysis of the UK trauma registry (TARN)
Bibliographic record
Abstract
OBJECTIVES: To identify the differences between women and men in the probability of entrapment, frequency of injury and outcomes following a motor vehicle collision. Publishing sex-disaggregated data, understanding differential patterns and exploring the reasons for these will assist with ensuring equity of outcomes especially in respect to triage, rescue and treatment of all patients. DESIGN: We examined data from the Trauma Audit and Research Network (TARN) registry to explore sex differences in entrapment, injuries and outcomes. We explored the relationship between age, sex and trapped status using multivariate logistical regression. SETTING: TARN is a UK-based trauma registry covering England and Wales. PARTICIPANTS: We examined data for 450 357 patients submitted to TARN during the study period (2012-2019), of which 70 027 met the inclusion criteria. There were 18 175 (26%) female and 51 852 (74%) male patients. PRIMARY AND SECONDARY OUTCOME MEASURES: We report difference in entrapment status, injury and outcome between female and male patients. For trapped patients, we examined the effect of sex and age on death from any cause. RESULTS: Female patients were more frequently trapped than male patients (female patients (F) 15.8%, male patients (M) 9.4%; p<0.0001). Trapped male patients more frequently suffered head (M 1318 (27.0%), F 578 (20.1%)), face, (M 46 (0.9%), F 6 (0.2%)), thoracic (M 2721 (55.8%), F 1438 (49.9%)) and limb injuries (M 1744 (35.8%), F 778 (27.0%); all p<0.0001). Female patients had more injuries to the pelvis (F 420 (14.6%), M 475 (9.7%); p<0.0001) and spine (F 359 (12.5%), M 485 (9.9%); p=0.001). Following adjustment for the interaction between age and sex, injury severity score, Glasgow Coma Scale and the Charlson Comorbidity Index, no difference in mortality was found between female and male patients. CONCLUSIONS: There are significant differences between female and male patients in the frequency at which patients are trapped and the injuries these patients sustain. This sex-disaggregated data may help vehicle manufacturers, road safety organisations and emergency services to tailor responses with the aim of equitable outcomes by targeting equal performance of safety measures and reducing excessive risk to one sex or gender.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".