892 BO38 – Spotlight on visually impaired (VI) athletes: a review of sport-related injuries, characteristics, risk factors, and opportunities for prevention
Bibliographic record
Abstract
Background Global sport participation is increasing among athletes with disabilities. Para sport athletes may experience unique injury-related challenges due to their impairments, which may necessitate tailored prevention strategies. Visually impaired (VI) athletes in particular, may be at higher injury risk. To support research planning for tailored primary prevention for VI athletes, understanding gaps in injury prevention literature is crucial. Objective To examine evidence on injury incidence, prevalence, characteristics, risk factors, and prevention strategies in VI athletes. Methods Four databases were comprehensively searched in March 2023 for articles that included VI athletes and injury as the main topic. Data related to incidence rates, prevalence, characteristics, risk factors, and prevention strategies were extracted. Results Studies predominantly focused on elite para sport competitions (e.g., Paralympic games, international, national, and world competitions). Geographically, most studies involved VI athletes in Europe (10, 43%) or South America (7, 30%). When reported, injury rates were heterogeneous (e.g., 1994 Canadian Blind Sports Association National Powerlifting Championships: 0.11 injuries/100 hours of training, 2018 Paralympic Judo World Championship: 34.9 injuries/1000 minutes). When compared to other para athletes, VI athletes often experienced more injuries. Specifically, 62% of sport-related concussions (SRC) occurred amongst athletes participating in VI sports (goalball: 31%, para swimming: 15%, judo: 15%), where all VI athletes’ SRCs were from collisions. Of the studies that identified injury mechanisms, overuse (30%, 20–82%) and collisions/contact (13%, 23–85%) were most common. Risk factors included training volume, para sport type, and visual classification. Prevention strategies were rarely evaluated; however, sport and injury specific strategies (i.e., swimming tappers to assist gauging the lane end) were discussed. Conclusions Most studies on VI athletes do not use prospective injury surveillance, vary in reporting standards, and are regionally limited. Primary studies examining injury rates, risk factors, and context-specific implementation factors are still needed to inform evidence-based prevention strategies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".