Attention problems as a risk factor for concussion in youth ice-hockey players
Bibliographic record
Abstract
Objective To examine attention problems in youth ice-hockey players as a risk factor for concussion. Design Secondary analysis of two prospective cohort studies. Setting Ice-rinks and Sport Medicine Centres in two Canadian cities over three ice-hockey seasons (2011/12, 2012/13, 2014/15). Assessment of risk factors Self-report of formal diagnosis of attention or learning disorders were collected on previously validated preseason baseline questionnaires by all participating players. Parents and players completed the Behaviour Assessment System for Children (BASC-2), which includes inattention and hyperactivity scales. Participants 2,364 Canadian ice-hockey players (11–17 years old, 87% male). Outcome measures A previously validated prospective injury surveillance system was used. All suspected concussions were referred to the study physician for confirmation of diagnosis. Concussion risk ratios (RR) were estimated, including stratification by known risk factors. Results One-hundred ninety-two of 2,215 players (9%) self-reported attention and/or learning disorders at baseline. Overall, 256 players (11%) sustained one or more concussions during a season. Players identified as “at-risk” for attention-deficit hyperactivity disorder (ADHD), based on BASC-2 T-scores (inattention and/or hyperactivity scale) greater than 60, had a greater risk of concussion during the season based on child report [RR=1.51 (95% CI 1.13–2.04)] and parent report [2.97 (95% CI 1.65–5.34)]. In players with no history of previous concussion, the RR based on child report was 1.88 (95% CI 1.22–2.90) and based on parent report was 5.03 (95% CI 2.45–10.29). Conclusions Ice-hockey players identified as “at-risk” for ADHD on the basis of baseline BASC-2 scores are at a greater risk of concussion. Competing interests None.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".