How much vision impairment does it take to decrease performance in freestyle swimming?
Bibliographic record
Abstract
Paralympic sport provides tremendous opportunities for individuals with impairment, including vision impairment, but there is controversy about the fairness of competition. Particularly, it remains unclear how much vision impairment should be necessary to compete. Almost all sports presently require visual acuity of at least 1.0 logMAR to qualify (20/200 or 6/60), though this cut-off is based on the legal definition of low vision and there is no evidence to suggest that this is a level of acuity that decreases sport performance. Moreover, the cut-off is likely to differ by sport. Accordingly, each sport could be including individuals without a disadvantage in the sport, or excluding those who do. The aim of this study was to establish the level of visual acuity loss that decreases performance in freestyle swimming. Twenty-one national level swimmers without vision loss swam 100m freestyle races in each of four different levels of vision impairment simulated using plus lenses (plano, +4.00, +6.00, +8.00). Visual acuities ranged from -0.3 to +1.6 logMAR. ROC analysis was used to establish the level of visual acuity that first brought performance below what would be expected by normal variation. Results revealed that a cut-off of at least 1.1 logMAR provided the optimal classification of performance as below expected performance (82% sensitivity and 68% specificity). In general, performance was not impacted by lesser amounts of vision impairment. The findings suggest that the minimum level of impairment required to compete in swimming for athletes with vision impairment may need to change, because there may be athletes competing whose impairment does not decrease performance in the sport. The results are expected to contribute to a change in the classification rules to be used for swimming in the 2028 Los Angeles Paralympic Games.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".