What Is Injury in Ice Hockey: An Integrative Literature Review on Injury Rates, Injury Definition, and Athlete Exposure in Men’s Elite Ice Hockey
Bibliographic record
Abstract
Injuries in men's elite ice hockey have been studied over the past 40 years, however, there is a lack of consensus on definitions of both injury and athlete exposure. These inconsistencies compromise the reliability and comparability of the research. While many individual studies report injury rates in ice hockey, we are not aware of any literature reviews that have evaluated the definitions of injury and athlete exposure in men's elite ice hockey. The purpose of this integrative review was to investigate the literature on hockey musculoskeletal injury to determine injury rates and synthesize information about the definitions of injury and athlete exposure. Injury rates varied from 13.8/1000 game athlete exposures to 121/1000 athlete exposures as measured by player-game hours. The majority of variability between studies is explained by differences in the definitions of both injury and athlete exposure. We were unable to find a consensus injury definition in elite ice hockey. In addition, we were unable to observe a consistent athlete exposure metric. We recommend that a consistent injury definition be adopted to evaluate injury risk in elite ice hockey. We recommend that injuries should be defined by a strict list that includes facial lacerations, dental injuries, and fractures. We also recommend that athlete exposure should be quantified using player-game hours.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".