Age-Related Eye Disease and Cognitive Function
Bibliographic record
Abstract
PURPOSE: Age-related eye disease may be associated with cognitive decline, but the scientific literature has not been consistent. Furthermore, no studies have been able to explain the relationship. Our objective was to assess whether older adults with age-related macular degeneration (AMD) or glaucoma performed worse on 6 cognitive tests compared with older adults with normal vision and, if so, to understand why. DESIGN: Cross-sectional analysis of hospital-based study (Maisonneuve-Rosemont Hospital Ophthalmology Clinics, Montréal, Canada). PARTICIPANTS: Three hundred thirty-six adults 65 years of age or older with either AMD, glaucoma, or normal vision. METHODS: Cognition was measured with 6 cognitive tests administered orally. Activity levels were measured using the Victoria Longitudinal Study Activity Lifestyle Questionnaire. Visual acuity and visual field were measured. Multiple linear regression was used. Mediation was assessed using structural equation modeling. MAIN OUTCOME MEASURES: Results of the verbal fluency test (animal and letter versions), the digit span test (forward and backward versions), and the logical memory test (immediate and delayed recall). RESULTS: People with glaucoma showed lower scores on 3 cognitive tests than the group with normal vision: the digit span forward and backward tests (β = -0.8 [95% confidence interval (CI), -1.5 to -0.2] and β = -0.7 [95% CI, -1.3 to -0.1], respectively) and the logical memory test with immediate recall (β = -1.3 [95% CI, -2.4 to -0.2]). Activity levels statistically significantly mediated the relationship between glaucoma and the digit span forward test (P = 0.043; percentage of the total effect mediated, 17%). CONCLUSIONS: People with glaucoma showed lower scores on cognitive tests that may depend on verbal working memory and encoding. If confirmed in longitudinal studies, interventions should be developed that are appropriate for a visually impaired population to slow this cognitive decline.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".