Parachute Canada/ThinkFirst Hockey Spinal Injuries Registry update
Bibliographic record
Abstract
Background: The Registry has collected data on spinal injuries in hockey for 30 years. This paper identifies the nature and incidence of spinal injuries in Canadian ice hockey and the impact of prevention programs. Methods: Data about spinal injuries with and without spinal cord injury in ice hockey have been collected by Parachute Canada/ThinkFirst’s Canadian Ice Hockey Spinal Injury Registry since 1981 through retrospective questionnaires from practitioners, ice hockey organizations and media reports. Injury risk factors assessed include age, gender, location, and injury mechanism. Results: From 1943-2011, 355 cases have been documented. Injuries were primarily sustained by males (97.7%), and were cervical (78.9%)in location, resulting mainly from impact with the boards (64.2%). Checking/pushing from behind (36.0%) was the most common cause of injury, although slightly lower during 2006-2011. Major differences between provinces continue; Ontario and Quebec, continue to show markedly different injury rates, with Ontario’s more than twice that of Quebec. Conclusions: Spinal injuries in hockey continue to occur, although at lower rates than in the peak years from 1981-2000. Injury prevention education and rules reinforcement (e.g. no checking/pushing from behind). Data indicate that multifaceted prevention programs have reduced the risk of injury.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 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) | 0.012 | 0.003 |
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".