A pilot survey on injury and safety concerns in international sledge hockey.
Bibliographic record
Abstract
OBJECTIVE: To describe sledge hockey injury patterns, safety issues and to develop potential injury prevention strategies. DESIGN: Pilot survey study of international sledge hockey professionals, including trainers, physiotherapists, physicians, coaches and/or general managers. SETTING: Personal encounter or online correspondence. RESPONDENTS: Sledge hockey professionals; a total of 10 respondents from the 5 top-ranked international teams recruited by personal encounter or online correspondence. MAIN OUTCOME MEASUREMENTS: Descriptive Data reports on sledge athlete injury characteristics, quality of rules and enforcement, player equipment, challenges in the medical management during competition, and overall safety. RESULTS: Muscle strains and concussions were identified as common, and injuries were reported to affect the upper body more frequently than the lower body. Overuse and body checking were predominant injury mechanisms. Safety concerns included excessive elbowing, inexperienced refereeing and inadequate equipment standards. CONCLUSIONS: This paper is the first publication primarily focused on sledge hockey injury and safety. This information provides unique opportunity for the consideration of implementation and evaluation of safety strategies. Safety interventions could include improved hand protection, cut-resistant materials in high-risk areas, increased vigilance to reduce intentional head-contact, lowered rink boards and modified bathroom floor surfacing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".