En-face choroidal vascularity in central serous chorioretinopathy
Bibliographic record
Abstract
PURPOSE: To evaluate the choroidal vascularity index of eyes for acute and chronic central serous chorioretinopathy patients using swept-source optical coherence tomography generated en-face scans. METHODS: This was a retrospective study, in which slabs of en-face optical coherence tomography scans, at 5 μm intervals, spanning from the retina to choroid, were binarized using a validated algorithm to calculate choroidal vascularity index. The choroidal vascularity index was defined as the ratio between the choroidal vascular luminal area and the total choroidal area. Choroidal vascularity index was calculated for all the slabs of every subject in both the groups. RESULTS: A total of 30 eyes for each acute and chronic central serous chorioretinopathy groups were recruited. The mean choroidal vascularity index of the acute group was 45.21% ± 2.25% at the choriocapillaris, which increased to the maximal value of 48.35% ± 2.06% at 75% depth of the choroidal thickness and 45.31% ± 3.27% at the choroidoscleral interface; whereas for the chronic group, the mean choroidal vascularity index was 44.76% ± 2.60% at the choriocapillaris, which maximized at 50% choroidal depth (48.70% ± 1.32%) and then returned to 45.41% ± 6.02% at the choroidoscleral interface. CONCLUSION: For both groups, the choroidal vascularity index increased from choriocapillaris to maximum values at mid-choroid and returned to almost the choriocapillaris value at the choroidoscleral interface.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".