Exploration of the minimum visual disability criteria for Para nordic and Para alpine skiing using simulated vision impairments
Bibliographic record
Abstract
The International Paralympic Committee Classification Code requires sports to develop evidence-based, sports-specific classification systems. This project aimed to determine the minimum eligibility criteria for Para nordic and Para alpine skiing by simulating vision impairments and measuring the impact of the impairments on performance in twenty-two nordic (28.09 ± 9.68 years; 16 male) and eleven alpine (37.91 ± 18.9 years, 11 male) able-sighted skiers. Eight visual acuity (VA)/contrast sensitivity (CS) (Cambridge Simulation Glasses, University of Cambridge) and six visual field (VF) impairments (bespoke goggles; University of Waterloo) were simulated. VA, CS and VF were measured in each participant before they completed an on-snow session, skiing short competition-style courses with each of the 14 simulated impairments in a randomized order. Clear goggle (no impairment) trials were used as controls. Receiver Operating Characteristic (ROC) and decision tree analyses were conducted to determine the optimum VA, CS and VF cut-offs for classifying performance based on differences from baseline in real time. Moderate impairments in VA, CS and VF negatively affect skiing performance. The recommended cut-off criteria based on both analyses were VA ≥0.90 logMAR and ≤48% VF extent for nordic and VA of ≥0.60 logMAR and VF extent of ≤59.0% for alpine.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".