Retinal Vessel Traits and Age‐Related Eye Disease in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: To cross-sectionally and longitudinally examine whether retinal vessel traits are associated with glaucoma-related outcomes (glaucoma, cup-to-disc ratio [CDR] and intraocular pressure [IOP]) and age-related macular degeneration (AMD). METHODS: Baseline and 3-year follow-up data from the 30 097 participants of the Canadian Longitudinal Study on Aging were used. The follow-up rate was 92%. QUARTZ, a deep learning algorithm, was used to extract data from retinal images including arteriolar and venular diameter, tortuosity and vertical CDR. Glaucoma and AMD were self-reported. IOP was measured. Multiple linear and logistic regression were used to adjust for demographic, lifestyle and clinical factors. RESULTS: Having wider arterioles was associated with a lower odds of glaucoma (OR = 0.36, 95% CI: 0.20, 0.65) at baseline but there was no association using longitudinal data. Instead, glaucoma at baseline was strongly associated with 3-year change in arteriolar diameter (β = -0.21, 95% CI: -0.37, -0.05) indicating that the cross-sectional association may have been due to reverse causality. Using longitudinal data, greater venular tortuosity was associated with a reduced 3-year development of glaucoma (OR = 0.52, 95% CI: 0.31, 0.87) and a 3-year reduction in the CDR (β = -0.006, 95% CI: -0.010, -0.002). Wider venular diameter was associated with a higher odds of AMD at baseline (OR = 2.77, 95% CI: 1.50, 5.15) and a higher odds of the 3-year development of AMD (OR = 4.15, 95% CI: 1.95, 8.82). CONCLUSIONS: Understanding the temporal relationship of changes in the retinal microvasculature and the development of eye disease may lead to better treatment and prevention strategies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".