Long-term retinal changes in progressive geographic atrophy
Bibliographic record
Abstract
Background: Age-related macular degeneration (AMD) is one of the leading causes of blindness with loss of retinal layers over long term. We aim to evaluate these changes in eyes with progressive non-exudative AMD with geographic atrophy (GA). Methods: This retrospective study included patients with GA with a minimum of 4 years follow up. Retinal layers on spectral domain optical coherence tomography (SD-OCT) were segmented based on their reflectivity patterns using validated semi-automated segmentation algorithm. The thickness of the segmented retinal layers was measured. Horizontal length of GA at baseline and last follow-up were also measured. Regression analysis was performed to correlate changes in RPE layer thickness with other retinal layers and the length of GA on OCT. Results: A total of 351-line scans including 17 foveal scans showing presence of GA at final visit that is, a total of 2457 retinal layer bands were analyzed. Outer nuclear layer (ONL) ( p = 0.02), outer segment layers (OSL) ( p = 0.01), and retinal pigment epithelium (RPE) ( p = 0.01) showed a statistically significant variation between baseline and final visit. Regression analysis showed the change in ONL ( r = 0.72; p = 0.01) and OSL ( r = 0.93, p < 0.01) correlated significantly with change in RPE thickness whereas rest of the layers failed to show significant correlation. Conclusion: Outer retinal layers (ONL and OSL) show more significant and widespread changes in retinal thickness and correlated most significantly with RPE thickness changes in eyes with GA due to AMD. Assessment of various retinal layer bands can be used as surrogate quantitative parameters to study eyes with GA.
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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 | 0.001 |
| 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) | 0.001 | 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".