A split decision based on vision: an empirical approach to classify short-distance sprinters with a vision impairment
Bibliographic record
Abstract
Athletes with vision impairment (VI) competing in track athletics typically compete in one of three ‘classes’ based on the severity of their VI. The creation of these classes, however, was arbitrary and not based on evidence. The aim of this study was to investigate the relationship between monocular visual acuity (VA) and sprinting performance in athletes with a VI to establish the optimal cut-off point(s) between competition classes. Historical data including VA and sprinting performance were collected through the International Paralympic Committee’s Sport Data Management System. Pearson correlations were conducted to investigate the relationship between VA and sprinting performance. VA was significantly associated with 100, 200 and 400 m race times (r = 0.26; r = 0.26; r = 0.24 [all p < 0.001], respectively). Decision tree analysis suggested splitting data into two classes with a VA cut-off at 2.1 logMAR. Stability assessment confirmed a split into two classes but showed considerable variability in the cut-off points between 2.1 and 3.2 logMAR. A two-class system would provide legitimate competition for athletes with VI in short-distance track athletics. However, other visual functions (e.g. contrast sensitivity) should be considered in future classification research.
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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 | 0.032 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".