Mean macular intercapillary area in eyes with diabetic macular oedema after <scp>anti‐</scp>vascular endothelial growth factor therapy and its association with treatment response
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Bibliographic record
Abstract
BACKGROUND: To evaluate the changes in the mean macular intercapillary area (ICA) from sequential enface optical coherence tomography angiography (OCTA) images following intravitreal anti-vascular endothelial growth factor (VEGF) therapy in initially treatment-naïve eyes with diabetic macular oedema (DME). METHODS: In this multicentre retrospective study, 6 × 6 and 3 × 3 mm customised, total retinal projection enface OCTA images were collected and processed for quantitative assessment of ICA by a customised MATLAB software. Measurements were done in concentric regions centred on the fovea-with the exclusion of foveal avascular zone (FAZ)-in 0.5 mm diameter increments as well as within the intervening rings. RESULTS: In this study, 6 × 6 mm OCTA images from 46 eyes of 29 patients, and 3 × 3 mm OCTA images from 23 eyes of 15 patients were included. There was no significant change in mean ICA after treatment in either scan size or in any measurement regions (all p > 0.05). Multivariate analysis revealed that baseline BCVA was significantly correlated with the visual outcome (p = 0.039). Additionally, after correction for age, baseline central retinal thickness (CRT), baseline BCVA, and retinopathy severity, mean ICA in the 1.5 mm circle was found to be a significant predictor of post treatment CRT, (p = 0.006). CONCLUSIONS: Absence of significant change in mean ICA after a minimum of three intravitreal anti-VEGF injections, may indicate that, in the short term, anti-VEGF injections neither impair nor improve macular perfusion in DME. Baseline BCVA was found to be a robust predictor of functional outcome, while inner mean ICA was a significant predictor for macular thickness outcomes.
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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 it