PREVALENCE AND RISK FACTORS FOR THE DEVELOPMENT OF PHYSICIAN-GRADED SUBRETINAL FIBROSIS IN EYES TREATED FOR NEOVASCULAR AGE-RELATED MACULAR DEGENERATION
Bibliographic record
Abstract
PURPOSE: To assess the prevalence and incidence of and risk factors for subretinal fibrosis (SRFi) in eyes with neovascular age-related macular degeneration (nAMD) that underwent vascular endothelial growth factor inhibitor treatment for up to 10 years. METHODS: A cross-sectional and longitudinal analysis was performed on data from a neovascular age-related macular degeneration registry. The presence and location of SRFi were graded by the treating practitioner. Visual acuity, lesion characteristics (type, morphology, and activity), and treatment administered at each visit was recorded. RESULTS: The prevalence of SRFi in 2,914 eyes rose from 20.4% at year interval 0-1 to 40.7% at year interval 9 to 10. The incidence in 1,950 eyes was 14.3% at baseline and 26.3% at 24 months. Independent characteristics associated with SRFi included poorer baseline vision (adjusted odds ratio 5.33 [95% confidence interval 4.66-7.61] for visual acuity ≤35 letters vs. visual acuity ≥70 letters, P < 0.01), baseline lesion size (adjusted odds ratio 1.08 [95% confidence interval 1.08-1.14] per 1000 µm, P = 0.03), lesion type (adjusted odds ratio 1.42 [95% confidence interval 1.17-1.72] for predominantly classic vs. occult lesions, P = 0.02), and proportion of active visits (adjusted odds ratio 1.58 [95% confidence interval 1.25-2.01] for the group with the highest level of activity vs. the lowest level of activity, P < 0.01). CONCLUSION: Subretinal fibrosis was found in 40% of eyes after 10 years of treatment. High rates of lesion activity, predominantly classic lesions, poor baseline vision, and larger lesion size seem to be independent risk factors for SRFi.
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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 | 0.003 |
| 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".