Multiple Past Concussions in High School Hockey Players: Examining Cognitive Functioning and Symptom Reporting
Bibliographic record
Abstract
OBJECTIVE: To investigate (1) if there are meaningful differences in baseline preseason cognitive functioning or symptom reporting between high school ice hockey players with and without prior concussions and (2) determine which health history variables predict symptom reporting. DESIGN: Cross-sectional study. SETTING: High schools across the state of Maine. PARTICIPANTS: Participants were 1616 male high school ice hockey players (mean age = 15.6 years; SD = 1.5 years) who completed baseline testing between 2009 and 2015. INDEPENDENT VARIABLES: Athletes were grouped according to their self-reported concussion history [0 (n = 1136), 1 (n = 321), 2 (n = 112), or 3+ (n = 47) previous concussions]. MAIN OUTCOME MEASURES: Cognitive functioning was measured by the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) battery, and symptom ratings were obtained from the Post-Concussion Symptom Scale. RESULTS: There were no statistically significant differences between groups in cognitive functioning as measured by ImPACT. The group with ≥3 prior concussions endorsed higher total symptom scores, but the effect sizes were small and not statistically significant. In a multivariate model, prior treatment for headaches (not necessarily migraines), prior treatment for a psychiatric condition, and prior treatment for substance/alcohol use all significantly predicted total symptom scores, with concussion history being the weakest independent predictor. CONCLUSIONS: Players with a history of prior concussions performed similarly to players with no prior concussions on cognitive testing. Health history factors were more strongly associated with symptom reporting than concussion history.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".