EXACERBATION OF CHOROIDAL AND RETINAL PIGMENT EPITHELIAL ATROPHY AFTER ANTI–VASCULAR ENDOTHELIAL GROWTH FACTOR TREATMENT IN NEOVASCULAR AGE-RELATED MACULAR DEGENERATION
Bibliographic record
Abstract
PURPOSE: To study the progression of retinal pigment epithelium (RPE) and choroidal atrophy in patients with neovascular age-related macular degeneration (AMD) and to assess for a possible association with the number and type of anti-vascular endothelial growth factor treatments. METHODS: Patients with neovascular AMD and a minimum of 1-year follow-up were reviewed. Fellow eyes with nonneovascular AMD were used as control eyes. Retinal pigment epithelial atrophy area and choroidal thickness were determined using spectral-domain optical coherence tomography. Multivariable regression models were used for statistical analyses. RESULTS: A total of 415 eyes were included in the study, with a mean follow-up of 2.2 years. Eyes with neovascular AMD had greater progression of RPE atrophy and choroidal atrophy compared with those with nonneovascular AMD (P < 0.001). Progression of RPE atrophy and choroidal atrophy was independently associated with the total number of injections of bevacizumab and ranibizumab (all P values ≤ 0.001). In the subgroup of 84 eyes with neovascular AMD and without RPE atrophy at baseline, only bevacizumab was associated with the progression of RPE atrophy (P = 0.003). This study likely lacked statistical power to detect an association with ranibizumab in this subgroup. CONCLUSION: Retinal pigment epithelial atrophy and choroidal atrophy in neovascular AMD seem to be exacerbated by anti-vascular endothelial growth factor treatment. Possible differences between bevacizumab and ranibizumab require further investigation.
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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 | 0.000 | 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".