Mayhem on the ice: do players' injuries put team staff at risk of injury?
Bibliographic record
Abstract
OBJECTIVE: To investigate the potential for serious injury and the nature of injuries incurred as team staff or support personnel cross ice surfaces to get to players' benches or to attend to injured players. DESIGN: Hybrid study, case series with survey. MAIN OUTCOME MEASURES: Circumstances and nature of reported injuries. RESULTS: Over 4 seasons, 988 injuries to team staff or support personnel were reported, including 94 concussions, 5 injuries to internal organs, 226 fractures, and 86 separations or dislocations. Most of the injuries were incurred by team staff or support personnel responsible for the welfare of players (managers, trainers, therapists, and emergency medical staff). CONCLUSION: Team staff and support personnel incur serious injuries as a result of falls on the ice. Several preventive strategies can be put in place: changes in rink design, policies restricting access to the ice surface, and encouraging team staff and support personnel who must cross the ice surface to attend to injured players to wear gait-stabilizing devices.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".