Coronary Artery Disease and Reticular Macular Disease, a Subphenotype of Early Age-Related Macular Degeneration
Bibliographic record
Abstract
PURPOSE: Reticular macular disease (RMD) is the highest risk form of early age-related macular degeneration and also specifically confers decreased longevity. However, because RMD requires advanced retinal imaging for adequate detection of its characteristic subretinal drusenoid deposits (SDD), it has not yet been completely studied with respect to coronary artery disease (CAD), the leading cause of death in the developed world. Because CAD appears in middle age, our purpose was to screen patients aged 45-80 years, documented either with or without CAD, to determine if CAD is associated with RMD. DESIGN: A prospective cohort study of patients with documented CAD status and no known retinal disease in a clinical practice setting at one institution. Subjects and Controls: A number of 76 eyes from 38 consecutive patients (23 with documented CAD, 15 controls documented without CAD; 47.4% female; mean age 66.7 years). METHODS: Patients were imaged with near-infrared reflectance/spectral domain optical coherence tomography and assessed in masked fashion by two graders for the presence of SDD lesions of RMD and soft drusen. MAIN OUTCOME MEASURES: Presence or absence of RMD/SDD and soft drusen. RESULTS: RMD was more frequent in patients with CAD versus those without (Relative Risk [RR] = 2.1, CI = 1.08-3.95, P = 0.03). There was no association of CAD with soft drusen. CONCLUSIONS: A specific relationship between CAD and RMD suggests common systemic causes for both and warrants further study.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".