Sex-based differences in outcomes for adult patients presenting to the emergency department with a concussion
Bibliographic record
Abstract
OBJECTIVE: Patients with concussion frequently present to the emergency department (ED). Studies of athletes and children indicate that concussion symptoms are often more severe and prolonged in females compared with males. Given infrequent study of concussion symptoms in the general adult population, the authors conducted a sex-based comparison of patients with concussion. METHODS: Adults (≥ 17 years of age) presenting with concussion to one of three urban Canadian EDs were recruited. Discharged patients were contacted by telephone 30 and 90 days later to capture the extent of persistent postconcussion symptoms using the Rivermead Post Concussion Symptoms Questionnaire (RPQ). A multivariate logistic regression model for persistent symptoms that included biological sex was developed. RESULTS: Overall, 250 patients were included; 131 (52%) were women, and the median age of women was significantly higher than that of men (40 vs 32 years). Women had higher RPQ scores at baseline (p < 0.001) and the 30-day follow-up (p = 0.001); this difference resolved by 90 days. The multivariate logistic regression identified that women, patients having a history of sleep disorder, and those presenting to the ED with concussions after a motor vehicle collision were more likely to experience persistent symptoms. CONCLUSIONS: In a community concussion sample, inconsequential demographic differences existed between adult women and men on ED presentation. Based on self-reported and objective outcomes, work and daily activities may be more affected by concussion and persistent postconcussion symptoms for women than men. Further analysis of these differences is required to identify different treatment options and ensure adequate care and management of injury.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".