Gender differences in clinical presentation and recovery of sports related concussion
Bibliographic record
Abstract
Objective To determine if gender plays a role in clinical presentation and recovery in concussion. Design Retrospective Setting one clinic Participants 207 athletes (115 females, 92 males) presenting to clinic from September 2014 – January 2016. 79 athletes achieved clearance. Inclusion: sports or exercise, age 10–60 yrs. Exclusion non-sports Outcome measures Athletes completed computerised neurocognitive testing and physical assessments. A physician-led interdisciplinary team treated each athlete until resolution of signs and symptoms. Athletes were grouped by gender into recovery time groups (0–2 mo.; 3–5 mo.;≥ 6 mo.). An analysis by gender, age, symptoms, neurocognitive data, and concussion features was performed. Main results Females report higher post-concussion symptom scores than males (p<0.0002).Females had a higher total number of features 4.5 to 3.6(p<0.00003). With respect to recovery, 34% of all males in our study were discharged within 0–2 mo. of injury date, compared to 12% of females. Females continued to experience symptoms at ≥ 6 months. Overall, females had a longer recovery period than males (p<0.002). Among discharged females, those requiring longer treatment (≥3 mo) were of a more mature age (23.8±13.2), presented later to clinic (t=3.3±3.6 mo.)and reported moresymptoms (PCSS=40.83±19.53). Conclusions Female athletes present with higher post- concussion symptom scores and clinical features than their male counterparts. Mature females who later for treatment have a protracted recovery. Athletes of both genders who seek care earlier in the injury process have a shorter recovery. Competing interests None.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".