What is the Injury Incidence and Profile in Professional Male Ice Hockey? A Systematic Review.
Bibliographic record
Abstract
BACKGROUND: Professional male ice hockey is characterized by a congested in-season match schedule and by different scenarios where the whole body is exposed to great internal and external forces. Consequently, injuries occur from head to toe. However, there is a lack of data synthesis regarding the injury incidence and profile in this population. PURPOSE: The aim of this study was to conduct a systematic review to quantify the injury incidence rates in professional male ice hockey. STUDY DESIGN: Systematic Review. METHODS: The electronic databases PubMed, CINAHL, Web of Science, ProQuest-Sport medicine & Education Index, and Pro-Quest Dissertation and Thesis were searched utilizing terms related to ice hockey and injuries. Studies were included if they provided the incidence of injury in professional male hockey players and reported injuries in terms of time lost. The modified Newcastle Ottawa Scale for cohort studies and the Strengthening the Reporting of Observational Studies in Epidemiology - Sports Injury and Illness Surveillance Statement were used to assess the methodological quality of the studies. RESULTS: Eleven studies were included in the review. Match injury incidence ranged from 38 to 88.6 injuries/1000 hours of exposure, whereas training injury incidence varied from 0.4 to 2.6 injuries/1000 hours of exposure. Injuries of traumatic origin accounted for 76% to 96.6% of all injuries, with contusions and lacerations being the most common. Severe injuries accounted for 7.8% - 20% of all injuries. The lower extremities were the most susceptible to injury, comprising 27% to 53.7% of all reported injuries. CONCLUSION: Professional male ice hockey players are exposed to a substantial risk of injury during competitions, with lower extremities being the most commonly affected body part. The majority of injuries are traumatic and severe injuries account for a notable portion of overall injury cases.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".