Window to the brain: Can retinopathy be used to assess cognitive function
Bibliographic record
Abstract
OBJECTIVE: Retinopathy status as a screening method to predict cognitive health is limited. The objective of this study was to examine the association between retinopathy and lowered cognitive performance in a Canadian First Nations population. METHODS: Eligible individuals were assessed by the Clock Drawing Test (CDT) and the Trail Making Test Parts A and B, which were combined into an executive function score (TMT-exec). Digital fundus photographs were taken for both eyes to assess retinopathy. Anthropometric, vascular and metabolic risk factors were assessed by interview, clinical examinations and blood tests. Carotid atherosclerosis was assessed by Doppler ultrasonography. RESULTS: Retinopathy was detected in 7.1% of the population. Individuals classified as having a previous history of cardiovascular disease, insulin resistance and diabetes were more likely to have retinopathy. No other cardiovascular risk factors were associated. In unadjusted analysis, there were no associations between retinopathy and lowered cognitive performance (CDT, odds ratio [OR]: 0.86, 95% confidence interval [CI]: 0.30–2.53; TMT-exec, OR: 1.79, 95% CI: 0.60–5.33). Multivariable adjusted analysis also showed no associations, although sample size may be limiting. CONCLUSIONS: Retinopathy was not associated with lowered cognitive performance. Associations for microvascular risk factors suggest a panel of cognitive tests is needed for future studies.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".