An Urgent Call for Concussion Incidence Measures in Para Sport for Athletes with Vision Impairment: A Narrative Review
Bibliographic record
Abstract
Concussion in para athletes with vision impairment (VI) is poorly understood. Recently published studies have suggested that athletes with VI may be more likely to sustain sport-related concussions compared to non-disabled athletes and athletes with other impairment types. There is a critical need for objective concussion incidence measures to determine concussion injury rates and risks more accurately. The aim of this review was to examine the limited available evidence of concussion incidence rates across six different para sports for athletes with VI and encourage the future collection of concussion incidence data and the adoption of injury prevention strategies in VI para sport. A literature search was conducted using four unique databases, which formed the basis of this narrative review. Injury prevention strategies such as modifying sport rules, introducing protective equipment, and incorporating additional safety measures into the field of play have been introduced sporadically, but the effectiveness of most strategies remains unknown. More prospective, sport-specific research examining mechanisms of injury and risk factors for concussion injuries in athletes with VI in both training and competition is needed. This research will help inform the development of targeted injury prevention strategies to reduce the likelihood of concussion for athletes with VI.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".