EFFECT OF PREVIOUS CONCUSSION OR INJURY ON SPORT-SPECIFIC SKILLS IN ELITE YOUTH ICE HOCKEY: A PILOT STUDY
Bibliographic record
Abstract
Background Concussions can lead to cognitive or neuromotor impairments which may influence skill performance. No published research has investigated the effect of previous concussions on sport-specific skill performance. Skill performance may be an important component of injury prevention strategy development in youth ice hockey. Objective To investigate the effect of previous concussion or injury on sport-specific skill performance in elite youth ice hockey. Design Cross Sectional pilot study. Setting Winsport arena in Calgary, Alberta, Canada. Participants: A sample of ten teams (n=131, 84 males and 47 females, ages 13-17) representing the most elite 20% of players by division of play (“AA, AAA”). Risk factor assessment Players completed a preseason baseline questionnaire including history of previous concussion and/or injury in the past year. Main outcome measurements On-ice skills testing was based on the Hockey Canada Skills Test (HCST) battery which included forward agility weave, forward and backward speed skate, forward to backward transition agility, and a 6-repeat endurance test. Results Comparing players with a history of concussion and those without, there were no differences in HCST component scores. There were also no differences in HCST scores between those reporting a previous injury within 1 year and those without. Males were faster than females on all HCST timed skill components, except for forward agility weave without the puck. Midget players were faster than Bantam players for transition agility with and without the puck, forward speed with the puck, and backward speed with and without the puck. Conclusion There were no apparent associations between history of concussion and/or injury and sport-specific skill performance. Our results provide reference values for HCST component scores that can allow assessment of risk and provide a foundation for prevention studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".