Choroidal vascularity profile in diabetic eyes using wide field optical coherence tomography
Bibliographic record
Abstract
Purpose To report the wide-field choroidal vascularity up to the mid-equator area in diabetic retinopathy (DR) subjects using wide-field optical coherence tomography (WF-OCT). Design Prospective, Cross-sectional study. Participants Forty-seven eyes of 25 DR subjects. Methods WF-OCT images (55 degrees) were obtained using Spectralis HRA + OCT (Heidelberg Engineering, Germany) in extremes of gazes in all quadrants and manual montages were created to obtain wide field images up to mid equator. A previously reported semi-automated algorithm was used to calculate choroidal vascularity profile (CVI). Regression analysis was performed to identify the factors influencing CVI. Results Forty-seven eyes from 25 patients were enrolled in the study. The mean age was 68.4 ± 10.6 years. The refractive error (spherical equivalent) ranged from −2.25 to +3.75 diopters. Most common DR grade among study subjects was moderate NPDR (29.41%) and 74.5% eyes had diabetic macular edema (DME). The mean CVI in the macular area (58.29 ± 3.63) was significantly lower than in any of the other fundus areas (all p ˂ 0.01). The maximum CVI was seen in the nasal region (66.60 ± 5.61), followed by temporal (65.69 ± 3.81), superior (65.01 ± 4.87), and inferior (63.80 ± 5.42). The vertical macular area had the least coefficient of variation (CV) of CVI (0.06) while the inferior quadrant had the highest CV (0.08). Conclusion The current study describes the CVI profile on WF-OCT in DR eyes up to mid-equator. The significant increase of the CVI compared to healthy subjects and its significant regional variations introduce this novel quantitative parameter as a reliable biomarker of the diabetes-induced choroidal microangiopathy.
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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 | 0.001 |
| 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".