A Systematic Review of the Association Between Body Checking and Injury in Youth Ice Hockey
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to systematically examine the risk of injury associated with body checking in youth ice hockey. DATA SOURCES: A systematic review of the relevant electronic databases was conducted including PubMed and Web of Science. The main search terms included "hockey, ice hockey, injury, body checking, child, adolescent, and pediatric." STUDY SELECTION: The initial search identified 898 potential articles, and, after verifying inclusion criteria, 260 articles were selected for further assessment. The Downs and Black instrument for nonrandomized studies (Downs 1998) was used to assess the quality of the articles. DATA EXTRACTION: Studies included reported on body checking as a mechanism of injury and compared injury rates in checking to non-checking leagues in children 20 years or younger. DATA SYNTHESIS: Twenty studies met the inclusion criteria and they predominantly found increased injuries associated with body checking. The relative risk of injury associated with body checking in comparative studies ranged from 0.6 to 39.8. Checking was the reported mechanism of injury between 2.9% and 91% of injuries. All but 1 study that met our inclusion criteria found an increased risk of injuries when body checking was permitted. CONCLUSIONS: Increased injuries attributable to checking were found where checking was allowed. This study supports policies that disallow body checking to reduce injuries in young children.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".