[no title]
Bibliographic record
Abstract
Concussions can lead to cognitive or neuromotor impairments which may influence skill performance.Few studies have investigated concussion and sports-specific skill performance, particularly in youth.Therefore, the purpose of this study was to examine previous concussion and components of the Hockey Canada Skills Test, a measure of ice hockey-specific skill performance, in youth ice hockey players (ages 11-17).A secondary purpose was todetermine the test-retest reliability of these components.Players completed a detailed baseline questionnaire on previous concussion history.On-ice measures included forward agility weave, forward/backward speed skate, transition agility, and a 6-repeat endurance skate (all measured in seconds).Multiple linear regression was conducted to examine history of concussion, number of previous concussions, time since most recent concussion, and severity of most recent concussion on on-ice performance.Test-retest reliability was assessed using intraclass correlation coefficients and mean differences with Bland-Altman Limits of Agreement.In total, 596 participants [525 males and 71 females, representing elite (upper 30% by division of play) and non-elite (lower 70%)] were recruited to examine the primary purpose.History of concussion (yes/no) and time since most recent concussion was not associated with any component.Players reporting 2 or more concussions were significantly faster than those with no previous concussion on forward agility weave with the puck.For every additional day to return to play post-concussion, player times were significantly faster on forward agility weave with and without the puck, transition agility without the puck, and backward speed with and without the puck.The intraclass correlation coefficients ranged from 0.50 to 0.92 and the Bland-Altman Limits of Agreement varied by component.These findings indicate players with and without history of concussion have similar on-ice scores, and that the components of the Hockey Canada Skills Test are a reliable measure of on-ice performance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".