Vision Training and Reaction Training for Improving Performance and Reducing Injury Risk in Athletes
Bibliographic record
Abstract
Visual processing, visual fields, and visual reaction times are essential to the performance of numerous sports and play a role in athletic injuries. Vision training, a process using visual exercises as part of a structured sports conditioning program, can be used to both enhance sports performance and prevent injury by improving neurovisual processing. In this review, evidence and methods concerning vision training programs are presented with the results suggesting performance enhancement and/or injury prevention, primarily concussion. Multiple studies are reviewed and utilized as examples that vision training programs designed to improve athletic performance or prevent injury are effective. We conclude from the collected evidence and theoretical considerations that vision training for numerous sports can be implemented with goals to improve performance and/or decrease injuries, specifically concussion. Key Points: 1) In this opinion paper we believe that vision training improves neurovisual processing. The vision training improves certain brain functions. 2) That vision training programs as part of athlete conditioning can improve athletic performance. Eye hand coordination, reaction times and peripheral awareness improve on the field of play. Obviously this benefit can be sport specific with some sports benefiting more than others. 3) There is emerging evidence that concussion rates can be decreased following pre-season vision training programs. The cause and effect needs to be better established and future research should address this opinion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".