Visual Contrast Sensitivity in Early-Stage Parkinson's Disease
Bibliographic record
Abstract
PURPOSE: Visual impairments are frequent in Parkinson's disease (PD) and impact normal functioning in daily activities. Visual contrast sensitivity is a powerful nonmotor sign for discriminating PD patients from controls. However, it is usually assessed with static visual stimuli. Here we examined the interaction between perception and eye movements in static and dynamic contrast sensitivity tasks in a cohort of mildly impaired, early-stage PD patients. METHODS: Patients (n = 13) and healthy age-matched controls (n = 12) viewed stimuli of various spatial frequencies (0-8 cyc/deg) and speeds (0°/s, 10°/s, 30°/s) on a computer monitor. Detection thresholds were determined by asking participants to adjust luminance contrast until they could just barely see the stimulus. Eye position was recorded with a video-based eye tracker. RESULTS: Patients' static contrast sensitivity was impaired in the intermediate spatial-frequency range and this impairment correlated with fixational instability. However, dynamic contrast sensitivity and patients' smooth pursuit were relatively normal. An independent component analysis revealed contrast sensitivity profiles differentiating patients and controls. CONCLUSIONS: Our study simultaneously assesses perceptual contrast sensitivity and eye movements in PD, revealing a possible link between fixational instability and perceptual deficits. Spatiotemporal contrast sensitivity profiles may represent an easily measurable metric as a component of a broader combined biometric for nonmotor features observed in PD.
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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 | 0.001 |
| 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".