CHORIOCAPILLARIS FLOW DEFICITS AS A RISK FACTOR FOR PROGRESSION OF AGE-RELATED MACULAR DEGENERATION
Bibliographic record
Abstract
PURPOSE: To evaluate the association between choriocapillaris (CC) flow deficits and structural optical coherence tomography biomarkers and the progression of intermediate age-related macular degeneration (iAMD) to complete retinal pigment epithelial and outer retinal atrophy. METHODS: Retrospective analysis of consecutive patients with iAMD with a minimum follow-up of 12 months. Odds ratios of intraretinal hyperreflective foci, hyporeflective drusen cores, subretinal drusenoid deposits, the presence of drusen volume ≥0.03 mm3 within a central 3-mm circle, fellow eye with late stage of AMD, and CC flow deficits at baseline and months of follow-up were estimated from logistic regression. RESULTS: A total of 112 eyes with iAMD were included. Eyes that progressed were significantly more likely to show intraretinal hyperreflective foci, hyporeflective drusen cores, and drusen volume ≥0.03 mm3. The CC flow deficit was also significantly greater in eyes that developed complete retinal pigment epithelial and outer retinal atrophy. Intraretinal hyperreflective foci, hyporeflective drusen cores, drusen volume ≥0.03 mm3, and higher CC flow deficits were significantly and independently associated with the development of complete retinal pigment epithelial and outer retinal atrophy. CONCLUSION: The CC flow deficit was significantly greater in iAMD eyes that progressed to complete retinal pigment epithelial and outer retinal atrophy and remained an independent risk factor when structural optical coherence tomography biomarkers were considered. CC flow deficits may be useful for enhancing risk stratification and prognostication of patients with iAMD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".