The Effect of Body Checking Policy Changes on Concussion Incidence in Canadian Male Youth Ice Hockey Players: A Critically Appraised Topic
Bibliographic record
Abstract
Clinical Scenario: Current studies have identified body checking as the most common cause of sports-related concussion in ice hockey across all divisions and levels. As a result, many hockey organizations, particularly in youth sports, have implemented rules making body checking to the head, face, and/or neck illegal. Such a rule, in Canada, makes age 13 the first age in which individuals can engage in body checking. Despite these changes, effectiveness of their implementation on the incidence of concussion in Canadian male youth ice hockey players remains unclear. Clinical Question: What is the effect of body checking policy changes on concussion incidence in male youth ice hockey players? Summary of Key Findings: Of the 3 included studies, 2 studies reported a decrease in the incidence of concussion once a body checking policy change was implemented. The third study showed an increase; however, it is important to note that this may be due, in part, to increased awareness leading to better reporting of injuries. Clinical Bottom Line: Current evidence supports a relationship between body checking policy implementation and decreased concussion incidence; however, more research is needed to understand the long-term implications of policy change and the effects in other leagues. In addition, further data are needed to differentiate between increased concussion incidence resulting from concussion education efforts that may improve disclosure and increased concussion incidence as a direct result of policy changes. Strength of Recommendation: Grade B evidence exists that policy changes regarding body checking decrease concussion incidence in male youth ice hockey players.
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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 | 0.027 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".