Evaluation of a Medically Supervised Concussion Protocol for Youth Hockey in Canada
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to evaluate the feasibility and implementation of a standardized medically supervised concussion protocol established between a city-wide AAA hockey league and a multi-disciplinary concussion program. METHODS: We conducted a retrospective review of injury surveillance, clinical and healthcare utilization data from all athletes evaluated and managed through the Winnipeg AAA Hockey concussion protocol during the 2016-2017 season. We also conducted post-season email surveys of head coaches and parents responsible for athletes who competed in the same season. RESULTS: During the 2016-2017 season, 28 athletes were evaluated through the medically supervised concussion protocol, with two athletes undergoing evaluation for repeat injuries (a total of 30 suspected injuries and consultations). In all, 96.7% of the athletes managed through the concussion protocol were captured by the league-designated Concussion Protocol Coordinator and 100% of eligible athletes underwent complete medical follow-up and clearance to return to full hockey activities. Although 90% of responding head coaches and 91% of parents were aware of the concussion protocol, survey results suggest that some athletes who sustained suspected concussions were not managed through the protocol. Head coaches and parents also indicated that athlete education and communication between medical and sport stakeholders were other elements of the concussion protocol that could be improved. CONCLUSION: Successful implementation of a medically supervised concussion protocol for youth hockey requires clear communication between sport stakeholders and timely access to multi-disciplinary experts in traumatic brain and spine injuries. Standardized concussion protocols for youth sports may benefit from periodic evaluations by sport stakeholders and incorporation of national guideline best practices and resources.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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".