Characteristics and Outcomes of Antivascular Endothelial Growth Factor Therapy in a Large Cohort of Patients With Pachychoroid Neovasculopathy
Bibliographic record
Abstract
Purpose: To examine the long-term visual and morphologic outcomes in a large series of patients with pachychoroid neovasculopathy treated with intravitreal antivascular endothelial growth factor (anti-VEGF) injections. Methods: A retrospective, observational study of anti-VEGF injections was performed in 249 eyes of 237 patients with pachychoroid neovasculopathy at 1 retina center over 7 years. Results: Mean patient age was 65.5 years and mean follow-up was 1.91 years (range, 3 months to 7.14 years). At baseline, mean best-corrected visual acuity (BCVA) was 20/60 Snellen, with an improvement of 2.30 Early Treatment Diabetic Retinopathy Study (ETDRS) letters by the study endpoint ( P = .04). From baseline to endpoint, mean central subfield thickness decreased by 78.2 μm, and mean choroidal thickness decreased by 35.4 μm (each P < .05). Treat-and-extend was utilized in 192 eyes (77.9%), with a 42.3% recurrence rate on extension. Treatment cessation was trialed in 70 eyes (28.1%), of which 53 eyes required no further treatment. Adjunct photodynamic therapy was utilized in 34 eyes (13.7%), resulting in a mean vision improvement of 0.44 ETDRS letters, compared with a mean improvement of 2.30 ETDRS letters in patients who received anti-VEGF only ( P = .6). Conclusions: Anti-VEGF therapy improved BCVA and anatomic features in this large cohort of patients with pachychoroid neovasculopathy. Continued treatment was required in 196 eyes (78.7%) at the study endpoint.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".