Classification in Para skiing: do better performing skiers have better visual functions?
Bibliographic record
Abstract
Introduction Currently, Paralympic skiers with vision impairment are allocated to classes based only on their better eye static visual acuity and visual field diameter. These studies were conducted to investigate whether a broad range of visual functions were different among groups of skiers with different levels of skiing performance. Methods Static and dynamic visual acuities, contrast sensitivity, light and glare sensitivity, glare recovery, motion perception, and visual field were assessed binocularly in elite Para nordic ( n = 26) and Para alpine skiers ( n = 15) at 3 international Paralympic events. Skiing performances were calculated using modified skiing points systems based on skiers' raw race times. Clusters of skiers with similar performances were identified in each sport, and their vision and non-vision variables were compared. Results Skiers in the best performing Para nordic clusters (1 and 2) had better static visual acuities ( p = 0.041) and larger visual fields ( p = 0.004) compared to cluster 3. In Para alpine slalom ( p = 0.019), giant slalom ( p = 0.019), and Super-G ( p = 0.039) the average static visual acuities among the better performing clusters were significantly better compared to the worst performing cluster. In slalom, the cluster with better performance also had a significantly larger visual field ( p = 0.038). In downhill, the better performance cluster demonstrated better dynamic visual acuity ( p = 0.029). Discussion Clusters with better performing skiers appear to have better visual function in both sports. The results of this study would suggest that Para nordic and Para alpine skiers with light perception or no light perception vision should be in one class and that the skiers with quantifiable static VA should be in a different class.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".