7.18 Increased symptom scores and prolonged course of recovery for female patients with concussion injuries seen at a university sport medicine clinic
Bibliographic record
Abstract
Objective To characterize concussion injuries seen at a university sport medicine clinic, comparing initial presentation and recovery between females and males, to identify sex-related differences. Design Retrospective chart review of patients with concussions seen by a Sport and Exercise Medicine (SEM) physician from January 1, 2015, to May 31, 2021. Setting University-based sport medicine clinic in Edmonton, Alberta, Canada. Participants 552 patients with 621 unique injuries (263F:358M; mean age 20±9 years) seen over 1,607 visits. Interventions (or Assessment of Risk Factors) Patients with concussions were subdivided by sex into female (F) or male (M). Outcome Measures Demographics, incidence, injury method, concussion severity, as measured by Standardized Concussion Assessment Tool (SCAT 3/5), and recovery timelines. Main Results In total, of 621 concussions, most resulted from organized sports (477), followed by recreational sports (82), then activities of daily living (ADL) (60), with 2 unspecified. Females were older (F 21.5±10.7 years, M 18.5±7.6 years, p<0.0001), with greater initial concussion severity, as measured by SCAT 3/5 symptoms and total scores (mean number of symptoms: F 14.2±6.5, M 10.0±6.8, p<0.00001; total symptom scores (TSS): F 40±28.1, M 24±23.7, p<0.00001). Females took longer to return to sport/activity (RTS): F 81.7±164.9 days, M 39.3±63.1 days, p=0.003; but not to return-to-learn (RTL): F 14.2±17.7 days, M 11.6±14.1 days, p=0.31). Conclusions In this cohort of patients, females reported greater concussion severity, with more symptoms at initial visit and took longer to return to sport/activity. Underlying mechanisms of injury and implications for therapy remain to be explored.
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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 | 0.001 |
| 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) | 0.006 | 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".