Age-Related Eye Disease and Cognitive Function
Bibliographic record
Abstract
PURPOSE: To determine whether people with age-related eye disease have lower cognitive scores than people with healthy vision. METHODS: A hospital-based cross-sectional study was performed in which 420 people aged 65 and older from the ophthalmology clinics at Maisonneuve-Rosemont Hospital (Montreal, Canada) were recruited who had age-related macular degeneration (AMD), Fuch's corneal dystrophy, or glaucoma. Patients with AMD and Fuchs had to have visual acuity in the better eye of worse than 20/40 while patients with glaucoma had to have visual field in their worse eye of at least -4 dB. Controls, recruited from the same clinics, did not have significant vision loss. Cognitive status was measured using the Mini-Mental State Exam Blind Version (range, 0-22) which excludes eight items that rely on vision. Linear regression with bootstrapped standard errors was used to adjust for demographic and medical factors. RESULTS: People with AMD, Fuch's corneal dystrophy, and glaucoma had lower cognitive scores, on average, than controls (P < 0.05). These relationships remained statistically significant after adjusting for factors such as age, sex, race, education, living alone, systemic comorbidities, and lens opacity. CONCLUSIONS: People with vision loss due to three different age-related eye diseases had lower cognitive scores. Reasons for this should be explored using longitudinal studies and a full battery of cognitive tests that do not rely on vision.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".