7.3 Sex differences in clinical presentation and recovery of sport-related concussion
Bibliographic record
Abstract
Objective To identify sex-related differences in clinical presentation and recovery in sports or recreation-related concussions. Design Medical record review. Setting Physician-led, interdisciplinary team in outpatient concussion clinic in Barrie, Ontario, Canada. Participants 665 consecutive patients (57.9% female) evaluated and treated from September 2014 – August 2020. Outcome Measures Sex-related differences between admission and discharge Post-concussion symptom scale scores (PCSS) and time to clinic discharge were measured using multivariate regression techniques. P-values ≤ 0.05 were statistically significant. Main Results Females presented with more severe concussion symptoms (PCSS of 41.5 (interquartile range (iqr) 21 to 60)) compared to males (PCSS 29 iqr 11 to 50)) (unadjusted mean difference = -10.0 (95% CI -13.7 to -6.2) (p<0.001). After adjusting for age, time from injury and first clinic visit, treatment duration and number of concussion features, the mean difference between female and male pre-post PCSS scores was -1.56 (95% CI -5.09–1.98) (p=0.387). Females (33.8%) were less frequently discharged from clinic than males (48.3%) (unadjusted mean difference=-14.% (95% CI -21.8—7.2) (p<0.001). After adjusting for age, admission PCSS, difference in pre-post PCSS, time from injury and first clinic visit, treatment duration, and number of concussion features, sex was not associated with concussion recovery duration and discharge (mean difference=1.9% (95% CI -7.0–3.2) (p=0.462). The only significant predictors for discharge were the admission PCSS (-0.142 (95% CI -0.178- -0.107)) (p<0.001) and differences in pre-post PCSS (-0.203 (95% CI -0.244- -0.162)) (p<0.001). Conclusions While females had more severe concussion symptoms, treatment outcomes did not differ from males.
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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 | 0.001 |
| 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) | 0.007 | 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".