Correlation of sectoral choroidal vascularity with angiographic leakage in central serous chorioretinopathy
Bibliographic record
Abstract
Purpose: To correlate sectoral choroidal vascularity with angiographic leakage in eyes with central serous chorioretinopathy (CSCR). Methods: This was a retrospective, cross-sectional study including patients with active CSCR. Multimodal imaging including fundus fluorescein angiography (FFA) and optical coherence tomography (OCT) were performed to identify leakage site and obtain choroidal measurements, respectively. An automated algorithm was used to perform shadow compensation, choroidal boundary localization and binarization, three (3-D) dimensional mapping, and early treatment of diabetic retinopathy study (ETDRS) grid based choroidal quantification that is, choroidal thickness (CT) and choroidal vascularity index (CVI). Nested analysis of variance (ANOVA) was performed to compare CT and CVI in different sectors. Results: Thirty-two eyes with active CSCR were analyzed. CT values varied significantly among the sectors (range, 450.27–482.63 µm; p = 0.005) and rings (range, 459.71–480.45 µm; p < 0.001), however, CVI failed to show significant variation among various segments (sectors, rings, and quadrants; range, 0.53–0.54; all p values > 0.05). Among 25 leaking spots in 25 different sectors, 12 (48%) had an increased CT compared to the overall CT whereas only 24% had increased CVI compared to overall CVI. Mean CT and CVI of the sectors with leakage (427.1 ± 81.1 µm; 0.51 ± 0.05) and remaining sectors without leakage (411.3 ± 73.9 µm; 0.53 ± 0.04) were not statistically different ( p = 0.48; p = 0.12, respectively). Conclusion: Though CT varied in different segments and increased CT corresponded to leakage points on FFA in 48% of eyes, CVI changes were more diffusely spread and local changes in CVI were not predictive of leakage location in eyes with active CSCR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".