The Effect of Sex, Sport, and Preexisting Histories on Baseline Concussion Test Performance in College Lacrosse and Soccer Athletes
Bibliographic record
Abstract
OBJECTIVE: To study sex and sport differences in baseline clinical concussion assessments. A secondary purpose was to determine if these same assessments are affected by self-reported histories of (1) concussion; (2) learning disability; (3) anxiety and/or depression; and (4) migraine. DESIGN: Prospective cohort. SETTING: National Collegiate Athletic Association D1 Universities. PARTICIPANTS: Male and female soccer and lacrosse athletes (n = 237; age = 19.8 ± 1.3 years). ASSESSMENT OF RISK FACTORS: Sport, sex, history of (1) concussion; (2) learning disability; (3) anxiety and/or depression; and (4) migraine. MAIN OUTCOME MEASURES: Sport Concussion Assessment Tool 22-item symptom checklist, Standardized Assessment of Concussion, Balance Error Scoring System (BESS), Generalized Anxiety Disorder 7-item scale, and Patient Health Questionnaire. RESULTS: Female athletes had significantly higher total symptoms endorsed (P = 0.02), total symptom severity (P < 0.001), and BESS total errors (P = 0.01) than male athletes. No other sex, sport, or sex-by-sport interactions were observed (P > 0.05). Previous concussion and migraine history were related to greater total symptoms endorsed (concussion: P = 0.03; migraine: P = 0.01) and total symptom severity (concussion: P = 0.04; migraine: P = 0.02). Athletes with a migraine history also self-reported higher anxiety (P = 0.004) and depression (P = 0.01) scores. No other associations between preexisting histories and clinical concussion outcomes were observed (P > 0.05). CONCLUSIONS: Our findings reinforce the need to individualize concussion assessment and management. This is highlighted by the findings involving sex differences and preexisting concussion and migraine histories. CLINICAL RELEVANCE: Clinicians should fully inventory athletes' personal and medical histories to better understand variability in measures, which may be used to inform return-to-participation decisions following 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".