The incidence of concussion in professional and collegiate ice hockey: are we making progress? A systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: The fast, random nature and characteristics of ice hockey make injury prevention a challenge as high-velocity impacts with players, sticks and boards occur and may result in a variety of injuries, including concussion. METHODS: Five online databases (January 1970 and May 2012) were systematically searched followed by a manual search of retrieved papers. RESULTS: Seventeen studies met the inclusion criteria. The heterogeneous diagnostic procedures and criteria for concussion prevented a pooling of data. When comparing the injury data of European and North American or Canadian leagues, the latter show a higher percentage of concussions in relation to the overall number of injuries (2-7% compared with 5.3-18.6%). The incidence ranged from 0.2/1000 to 6.5/1000 game-hours, 0.72/1000 to 1.81/1000 athlete-exposures and was estimated at 0.1/1000 practice-hours. DISCUSSION AND CONCLUSIONS: The included studies indicate a high incidence of concussion in professional and collegiate ice hockey. Despite all efforts there is no conclusive evidence that rule changes or other measures lead to a decrease in the actual incidence of concussions over the last few decades. This review supports the need for standardisation of the diagnostic criteria and reporting protocols for concussion to allow interstudy comparisons in the future.
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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 | 0.013 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".