Color Discrimination Performance in Patients with Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND/AIMS: Visual deficits are frequent in Alzheimer's disease (AD), yet little is known about the nature of these disturbances. The aim of the present study was to investigate color discrimination in patients with AD to determine whether impairment of this visual function is a cognitive or perceptive/sensory disturbance. METHODS: A cross-sectional clinical study was conducted in a specialized dementia unit on 20 patients with mild/moderate AD and 21 age-matched normal controls. Color discrimination was measured by the Farnsworth-Munsell 100 hue test. Cognitive functioning was measured with the Mini-Mental State Examination (MMSE) and a comprehensive battery of neuropsychological tests. The scores obtained on the color discrimination test were compared between AD patients and controls adjusting for global and domain-specific cognitive performance. RESULTS: Color discrimination performance was inversely related to MMSE score. AD patients had a higher number of errors in color discrimination than controls (mean +/- SD total error score: 442.4 +/- 84.5 vs. 304.1 +/- 45.9). This trend persisted even after adjustment for MMSE score and cognitive performance on specific cognitive domains. CONCLUSIONS: A specific reduction of color discrimination capacity is present in AD patients. This deficit does not solely depend upon cognitive impairment, and involvement of the primary visual cortex and/or retinal ganglionar cells may be contributory.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".