Abnormal Visual Contrast Acuity in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Low-contrast vision is thought to be reduced in Parkinson's disease (PD). This may have a direct impact on quality of life such as driving, using tools, finding objects, and mobility in low-light condition. Low-contrast letter acuity testing has been successful in assessing low-contrast vision in multiple sclerosis. We report the use of a new iPad application to measure low-contrast acuity in patients with PD. OBJECTIVE: To evaluate low- and high-contrast letter acuity in PD patients and controls using a variable contrast acuity eye chart developed for the Apple iPad. METHODS: Thirty-two PD and 71 control subjects were studied. Subjects viewed the Variable Contrast Acuity Chart on an iPad with both eyes open at two distances (40 cm and 2 m) and at high contrast (black and white visual acuity) and 2.5% low contrast. Acuity scores for the two groups were compared. RESULTS: PD patients had significantly lower scores (indicating worse vision) for 2.5% low contrast at both distances and for high contrast at 2 m (p < 0.003) compared to controls. No significant difference was found between the two groups for high contrast at 40 cm (p = 0.12). CONCLUSIONS: Parkinson's disease patients have reduced low and high contrast acuity compared to controls. An iPad app, as used in this study, could serve as a quick screening tool to complement more formal testing of patients with PD and other neurologic disorders.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".