MANAGEMENT OF RETINAL PIGMENT EPITHELIUM TEAR DURING ANTI–VASCULAR ENDOTHELIAL GROWTH FACTOR THERAPY
Bibliographic record
Abstract
PURPOSE: This article aims to review current evidence on the development, diagnosis, and management of retinal pigment epithelium (RPE) tear during anti-vascular endothelial growth factor (VEGF) therapy. METHODS: Literature searches were performed using MEDLINE/PubMed databases (cut-off date: August 2019). RESULTS: Three key recommendations were made based on existing literature and clinical experience: 1) Multimodal imaging with color fundus photography, optical coherence tomography, near-infrared reflectance imaging, fundus autofluorescence imaging, optical coherence tomography-angiography, and/or fluorescein angiography are recommended to diagnose RPE tear and assess risk factors. Retinal pigment epithelium tears can be graded by size and foveal involvement. 2) Patients at high risk of developing RPE tear should be monitored after each anti-VEGF injection. If risk factors worsen, it is not yet definitively known whether anti-VEGF administration should be more frequent, or alternatively stopped in such patients. Prospective research into high-risk characteristics is needed. 3) After RPE tear develops, anti-VEGF treatment should be continued in patients with active disease (as indicated by presence of intraretinal or subretinal fluid), although cessation of therapy should be considered in eyes with multilobular tears. CONCLUSION: Although evidence to support the assumption that anti-VEGF treatment contributes to development of RPE tear is not definitive, some data suggest this link.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".