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
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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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 | 0.001 | 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".