CORRELATION OF VISUAL ACUITY WITH FIBROTIC SCAR LOCATION IN TREATED NEOVASCULAR AGE-RELATED MACULAR DEGENERATION EYES
Bibliographic record
Abstract
PURPOSE: To determine whether the optical coherence tomography location of a subfoveal fibrovascular scar is correlated with visual outcome in eyes successfully treated with antivascular endothelial growth factor agents for neovascular age-related macular degeneration. METHODS: Fifty-six eyes from 56 patients with a subfoveal disciform scar after antivascular endothelial growth factor treatment were included. The initial and final visual acuity, fluorescein angiography, and spectral domain optical coherence tomography scar characteristics were retrospectively reviewed. RESULTS: Thirty-five of 56 eyes (62.5%) were classified as having entirely subretinal pigment epithelial (sub-RPE) scars, and 21 eyes (37.5%) had subretinal component scars. Mean initial visual acuity was similar between sub-RPE and subretinal scars (20/100 vs. 20/125, P = 0.517); mean final visual acuity was better in the sub-RPE scar group (20/60 vs. 20/200, P = 0.001). Eyes with sub-RPE scar had better preservation of the external limiting membrane, ellipsoid layer, and retinal thickness (P < 0.001, P = 0.017, P = 0.004, respectively) than subretinal component scar eyes. There was no difference between the groups in scar thickness or scar area (P = 0.707, P = 0.186, respectively). CONCLUSION: Sub-RPE location of subfoveal scarring in eyes treated for neovascular age-related macular degeneration is associated with better preservation of outer retinal structures and better vision, when compared with a subretinal scar.
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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 | 0.004 |
| 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 | 0.000 |
| 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".