Are Self-Reported and Parent-Reported Attention Problems and Hyperactivity Associated With Higher Rates of Concussion in Youth Ice Hockey Players?
Bibliographic record
Abstract
OBJECTIVE: To examine the association between self-reported and parent-reported attention problems and hyperactivity and rates of injury and concussion in Canadian youth ice hockey players. DESIGN: Secondary analyses of 2 prospective cohort studies. SETTING: Canadian youth ice hockey teams. PARTICIPANTS: Ice hockey players (ages 11-17 years) were recruited by team, over 4 seasons (2011-2016). A combined 1709 players contributing 1996 player-seasons were analyzed (257 players participated in more than one season). ASSESSMENT OF RISK FACTORS: Data were collected from preseason baseline questionnaires, including child and parent proxy forms of the Behavior Assessment System for Children, second edition. MAIN OUTCOME MEASURES: Injury and concussion rates and incidence rate ratios (IRR) comparing players with and without self-identified or parent-identified attention problems and hyperactivity, adjusted for covariates (ie, body checking policy, previous injury/concussion, and age) and a random effect for team, were estimated using multiple multilevel negative binomial regression. RESULTS: When analyzed continuously, rates of concussion increased with higher self-reported and parent-reported measures of attention problems [IRR SELF = 1.025; 95% confidence interval (CI): 1.011-1.040; IRR PARENT = 1.032; 95% CI: 1.008-1.057]. Self-reported hyperactivity was significantly associated with concussion (IRR = 1.021; 95% CI: 1.007-1.035), but parent-reported hyperactivity was not (IRR = 1.005; 95% CI: 0.983-1.028). A T score ≥ 60 cutoff combining attention problems and hyperactivity scores (an estimate of probable attention-deficit hyperactivity disorder) was not significantly associated with rates of injury or concussion. CONCLUSIONS: Attention problems and hyperactivity may place youth ice hockey players at increased risk of concussion and injury. Preseason assessments could identify players for targeted concussion education and risk reduction strategies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".