Spinal Injuries in Canadian Ice Hockey: An Update to 2005
Bibliographic record
Abstract
OBJECTIVE: Measure incidence of spinal injuries in Canadian ice hockey for the 6-year period 2000-2005 and examine trends from 1943 to 2005. DESIGN: Data about spinal injuries with and without spinal cord injury in ice hockey have been collected by ThinkFirst's Canadian Ice Hockey Spinal Injuries Registry since 1981 through questionnaires from practitioners, ice hockey organizations, and media reports. SETTING: All provinces and territories of Canada. PARTICIPANTS: All Canadian ice hockey players. ASSESSMENT OF RISK FACTORS: Age, gender, level of play, location, and mechanism of injury. MAIN OUTCOME MEASURES: Incidence and nature of injuries. RESULTS: Forty cases occurred in 2000-2005, representing a decline in annual injuries and bringing the total registry cases to 311 during 1943-2005. Five (12.5%) of these 40 cases were severe, which includes all complete and incomplete spinal cord injuries, and is a decline from the previous 23.5% in this category. In the 311 cases, men comprised 97.7%, the median age was 18 years, 82.8% of the injuries were cervical, and 90.3% occurred in games in organized leagues. The most common mechanism of injury was impact with the boards (64.8%), and the most common cause was check/push from behind at 35.0%, which has declined. The major provincial differences in injury rates persist, with the highest in Ontario, British Columbia, New Brunswick, and Prince Edward Island and the lowest in Quebec and Newfoundland. CONCLUSIONS: There has been a recent decline in spinal injuries in Canadian ice hockey that may be related to improved education about injury prevention and/or specific rules against checking/pushing from behind.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".