Association between Primary Open-Angle Glaucoma and Cognitive Impairment as Measured by the Montreal Cognitive Assessment
Bibliographic record
Abstract
BACKGROUND: It is currently unclear whether primary open-angle glaucoma (POAG) affects neurological functions outside of vision, such as cognition. OBJECTIVE: This study examined the association between POAG and cognitive impairment in African Americans. METHODS: Masked interviewers administered the Montreal Cognitive Assessment (MoCA) to patients enrolled in the Primary Open-Angle African American Glaucoma Genetics (POAAGG) study at the Scheie Eye Institute. Cases were further assessed for retinal nerve fiber layer (RNFL) thickness and visual field (VF) loss. Univariate and multivariate linear regression analyses were performed to compare mean MoCA score between cases and controls and to assess the association between POAG severity and MoCA score. RESULTS: A total of 137 patients completed the MoCA, including 70 cases and 67 controls. The mean age ± SD was 68.7 ± 11.2 years for cases and 65.7 ± 10.4 years for controls (p = 0.11). The mean MoCA total score (out of 30 points) was 20.3 among POAG cases and 21.3 among controls (mean difference = -1.03, 95% confidence interval, CI = -2.54 to 0.48, p = 0.18). After adjusting for age, gender, education level, diabetes, hypertension, and smoking status, the mean difference in the MoCA total score between cases and controls was -0.64 (95% CI = -1.72 to 0.45, p = 0.25). Among cases, more VF loss was associated with lower total MoCA score for mean deviation (adjusted linear trend p = 0.02) and VF index (adjusted linear trend p = 0.03). There was no significant association between average RNFL thickness and total MoCA score. CONCLUSIONS: POAG cases and controls had similar neurocognitive function as measured by the MoCA. Among POAG cases, worse VF loss was associated with lower MoCA. Future studies are needed to further elucidate the clinical effect of neuropathy in POAG.
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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 | 0.003 |
| 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 | 0.000 | 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".