Topical Review: Understanding Vision Impairment and Sports Performance through a Look at Paralympic Classification
Bibliographic record
Abstract
SIGNIFICANCE: To provide meaningful competition that is equitable for Paralympic athletes, classification systems are vital to determine which athletes are eligible to compete in adapted forms of sports and to group athletes for competition. Our discussion has important implications to inform how we should approach visual function assessment in sports performance. Sport participation positively benefits individuals with low vision. In particular, adapted sports exist to provide people with visual disabilities an avenue for participating in recreational activity. High-performance low-vision athletes can participate in Paralympic sports but need to be properly classified based on the severity of their vision impairment. The model for Paralympic classification was initiated by Sir Ludwig Guttmann in 1952 in a rehabilitation clinic for soldiers with spinal cord injuries. Today, the International Paralympic Committee mandates that international sports federations develop evidence-based sport-specific classification systems to ensure that eligible disabled athletes have an opportunity for meaningful competition. With the current classification system, only visual acuity and visual field measures are considered to determine an athlete's eligibility to compete, leaving room to expand our understanding of visual function requirements for individual sports. In this topical review, we discuss the origins of Paralympic sports, limitations of current classification methods, and requirements toward achieving evidence-based sport-specific evaluation systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".