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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".