Macular hole after anti-vascular endothelial growth factor injection: A review
Bibliographic record
Abstract
INTRODUCTION: Anti-vascular endothelial growth factor (anti-VEGF) injections are a crucial treatment for neovascular age-related macular degeneration (nvAMD); however, each injection carries the risk of complications, including macular hole (MH) formation. A comprehensive literature search of the PubMed, Embase, and Google Scholar databases identified studies reporting MH formation after anti-VEGF injections for nvAMD. Demographic characteristics included age, sex and affected eye. The presence of intraretinal fluid, subretinal fluid (SRF), pigment epithelial detachment (PED), posterior vitreous detachment, epiretinal membrane, vitreomacular adhesion (VMA), vitreomacular traction (VMT), and retinal pigment epithelial (RPE) tears was recorded. This review includes 15 articles, encompassing 50 eyes. Median patient age was 76.0 years, and 54 % were female. Prior to MH formation, SRF and PED were present in 68 % of eyes. VMA or VMT was observed in 36 % of eyes and the median number of injections before MH development was 3, with a median time to diagnosis of 60 days. While anti-VEGF is an essential therapy, clinicians must be aware of the risk of MH Predisposing factors including PEDs, VMT, and significant intra- or subretinal fluid, should be considered. Further research is needed to fully understand the mechanisms and explore preventative strategies.
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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 | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".