Hormonal Contraceptives Do Not Influence Concussion Recovery in Collegiate Athletes: Data from the NCAA-DoD CARE Consortium
Bibliographic record
Abstract
INTRODUCTION: The hormonal withdrawal hypothesis suggests that progesterone reduction in women after concussion may lead to greater symptom burden and longer recoveries. Current evidence indicates that hormonal stability after head injury may be an important moderator of postconcussive recovery. Thus, female athletes using hormonal contraceptives (HC) may exhibit better recovery profiles as their hormone levels are artificially stabilized. Our investigation sought to examine the relation between HC use and concussion outcomes in female student-athletes. METHODS: This longitudinal study examined concussion outcomes from female student-athletes participating in the NCAA-DoD CARE Consortium Research Initiative, including academic years 2014 to 2020. Eighty-six female collegiate athletes reporting HC use (HC+) were group matched on age, body mass index, race/ethnicity, sport contact level, concussion history, and current injury characteristics (i.e., amnesia, loss of consciousness) to 86 female collegiate athletes reporting no HC use (HC-). All participants had sustained a concussion and completed the Sport Concussion Assessment Tool, 3rd edition Symptom Scale, Brief Symptom Inventory-18, and Immediate Post-concussion Assessment and Cognitive Testing at preinjury baseline, 24 to 48 h postinjury, and when cleared for unrestricted return to play. To provide an index of recovery trajectory, days between injury and unrestricted return to play were calculated. RESULTS: Groups did not differ on length of recovery, postconcussion symptoms, psychological health, or cognitive assessments. No differences were observed between groups on any measure when accounting for baseline levels of performance. CONCLUSIONS: Our findings suggest that HC use does not influence recovery trajectory, symptoms, or recovery of cognitive function after concussion.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".