One-year outcomes of ziv-aflibercept for macular edema in central retinal vein occlusion
Bibliographic record
Abstract
Purpose To report the 12-month efficacy and safety outcomes of intravitreal ziv-aflibercept in macular edema secondary to central retinal vein occlusion (CRVO). Methods Interventional case series documenting 12-month outcomes of intravitreal ziv-aflibercept (1.25 mg in 0.05 mL) in 6 patients with treatment-naive macular edema secondary to CRVO. All patients had comprehensive ophthalmic examination, spectral domain optical coherence tomography at baseline and all follow-up visits, and fluorescein. Retreatment decisions were based on recurrence or persistence of intraretinal or subretinal fluid, deterioration in visual acuity (VA), increase in central subfield thickness (CST) by ≥ 50 μm from the previous visit, or lowest recorded CST. Results Participants had (2 males, 4 females) an average age of 53.5 years. From baseline to 12 months, the mean logMAR VA improved from 0.86 (Snellen ≈ 20/145) to 0.33 (Snellen ≈ 20/40), central macular thickness decreased from 519 μm to 255 μm, and total macular volume decreased from 14.7 mm 3 to 7.1 mm 3 . No eyes had uveitis, cataract progression, intraocular pressure (IOP) elevations, or systemic adverse events. Conclusions and importance Ziv-aflibercept achieves favorable intermediate-term functional and structural outcomes in macular edema secondary to CRVO. No safety concerns were raised. Low-cost ziv-aflibercept may thus be useful for CRVO in resource-poor countries. Further prospective studies in larger cohorts are needed further establish the efficacy and safety of this agent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".