Factors Associated With Concussion-like Symptom Reporting in High School Athletes
Bibliographic record
Abstract
IMPORTANCE: Every state in the United States has passed legislation for sport-related concussion, making this health issue important for physicians and other health care professionals. Safely returning athletes to sport after concussion relies on accurately determining when their symptoms resolve. OBJECTIVE: To evaluate baseline concussion-like symptom reporting in uninjured adolescent student athletes. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional, observational study, we studied 31 958 high school athletes from Maine with no concussion in the past 6 months who completed a preseason baseline testing program between 2009 and 2013. RESULTS: Symptom reporting was more common in girls than boys. Most students with preexisting conditions reported one or more symptoms (60%-82% of boys and 73%-97% of girls). Nineteen percent of boys and 28% of girls reported having a symptom burden resembling an International Classification of Diseases, 10th Revision (ICD-10) diagnosis of postconcussional syndrome (PCS). Students with preexisting conditions were even more likely to endorse a symptom burden that resembled PCS (21%-47% for boys and 33%-72% for girls). Prior treatment of a psychiatric condition was the strongest independent predictor for symptom reporting in boys, followed by a history of migraines. For girls, the strongest independent predictors were prior treatment of a psychiatric condition or substance abuse and attention-deficit/hyperactivity disorder. The weakest independent predictor of symptoms for both sexes was history of prior concussions. CONCLUSIONS AND RELEVANCE: In the absence of a recent concussion, symptom reporting is related to sex and preexisting conditions. Consideration of sex and preexisting health conditions can help prevent misinterpretation of symptoms in student athletes who sustain a 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".