Concussions and Injuries in Sledge Hockey: Grassroots to Elite Participation
Bibliographic record
Abstract
OBJECTIVE: To examine injury (including concussion) rates, location, type, mechanisms, and risk factors in sledge hockey players. DESIGN: Cross-sectional survey. SETTING: Sledge hockey players, worldwide, across all levels of play. PARTICIPANTS: Sledge hockey players (ages ≥14 years) who played in the 2019 to 2020 season were recruited through email, social media, and word of mouth communication. ASSESSMENT OF RISK FACTORS: Participant characteristics (eg, age, sex, disability) were examined as potential injury risk factors. MAIN OUTCOME MEASURES: Injury rates (IR) and incidence rate ratios (IRR) examining potential risk factors were reported based on univariate Poisson regression analyses. Injury proportions by type, location, and mechanism were described. RESULTS: Ninety-two players initiated the survey, and 77 (83.7%) provided some injury information. Forty-seven injuries included 16 concussions in 9 of 77 players (11.7%) and 31 non-concussion injuries in 20 of 77 players (26.0%) were reported. The overall IR was 13.2 injuries/1000 athlete-exposures [95% confidence interval (CI); 9.6-17.6]. The game IR (28.4 injuries/1000 game-exposures, 95% CI; 18.6-41.7) was higher than practice IR (4.4 injuries/1000 practice-exposures, 95% CI; 2.2-7.9) (IRR = 6.5, 95% CI; 3.1-14.5). The most common injury locations were the head (34.0%), wrist/hand (14.8%), and shoulder (10.6%). The most common significant injury types were concussion (36.2%) and bone fracture (8.5%). Body checking was the primary mechanism for injuries caused by contact with another player (42.1%) Age, sex, disability, and level of play were not found as injury risk factors. CONCLUSIONS: Concussions and upper extremity injuries were the most common sledge hockey injuries reported, with body checking being the most common mechanism. This research will inform development of prevention strategies in sledge hockey.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".