Pachychoroid‐phenotype effects on 5‐year visual outcomes of anti‐VEGF monotherapy in polypoidal choroidal vasculopathy
Bibliographic record
Abstract
PURPOSE: To investigate whether the efficacy of anti-vascular endothelial growth factor (VEGF) monotherapy for polypoidal choroidal vasculopathy (PCV) differs between pachychoroid and non-pachychoroid phenotypes in the long term. METHODS: This retrospective longitudinal study included 115 treatment-naïve eyes in 115 consecutive patients with symptomatic PCV who were treated with anti-VEGF monotherapy and were followed up for 5 years. Eligible eyes were assigned to either a pachy-PCV group, with a pachychoroid phenotype, or a non-pachy-PCV group, without a pachychoroid phenotype. Best-corrected visual acuity (BCVA) and other parameters over a 5-year period were compared between the groups. RESULTS: Forty-eight eyes and 67 eyes were classified into the pachy-PCV and non-pachy-PCV groups respectively. Baseline and 5-year BCVA (logarithm of the minimum angle of resolution) were 0.19 ± 0.20 and 0.16 ± 0.28 in the pachy-PCV group, respectively, and 0.25 ± 0.26 and 0.26 ± 0.36 in the non-pachy-PCV group respectively. BCVA did not change significantly in either group (p = 0.18 and 0.08 respectively). BCVA did not differ between the groups at any observation time-point. Subfoveal choroidal thickness (SFCT) at baseline and at 5 years was significantly higher in the pachy-PCV group than in the non-pachy-PCV group (both p < 0.001); however, the mean rate of decrease in SFCT did not differ in either group over the 5-year period (22% vs. 23%, p = 0.81). CONCLUSION: Our findings suggest that anti-VEGF monotherapy was similarly effective for pachychoroid- and non-pachychoroid-phenotype eyes with PCV, for at least 5 years, although further studies are required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".