The Epidemiology of Injuries in Ice Hockey Athletes
Bibliographic record
Abstract
Background: There is a lack of current research into the mechanism, incidence, and rate of all injuries within women’s ice hockey, especially when trying to compare injuries with men’s ice hockey. This narrative review explores the research pertaining to the incidence of ice hockey injuries, the anatomical location of injury, the mechanism of injury, and the diagnosis of injury. Methods: Articles were found using either PubMed, Google scholar, or through using the University of Pittsburgh Health Sciences Library System by using a combination of search terms, including “ice hockey”, “women’s”, “men’s”, “injuries”, “epidemiology”, “incidence of injury”, “collegiate sport”, “NCAA”, “Canadian”, and “contact sports”. Results: Injury rates in ice hockey range from 2.0 injuries per 1000 Athlete Exposures (AEs) to 27.0 injuries per 1000 AEs. Data collected shows a variety in the mechanism and diagnosis of injury. Contact with another player is a common mechanism of injury in both women’s and men’s ice hockey, regardless of age or level of competition. Strains and sprains are a common diagnosis of injury for both women’s and men’s ice hockey, and concussions tend to be more common in women’s ice hockey when compared to men’s ice hockey at a similar level. Conclusion: In conclusion, this narrative review highlights the need for ongoing research into the mechanism, incidence, and rate of injuries in women's ice hockey, particularly when compared to men's ice hockey. The results of the study suggest that injury rates in ice hockey vary greatly and that contact with another player is a common mechanism of injury in both women's and men's ice hockey. Strains, sprains, and concussions are common diagnoses in both women's and men's ice hockey. Further research is needed to better develop effective injury prevention strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".