OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY MORPHOLOGY AFTER RHEGMATOGENOUS RETINAL DETACHMENT REPAIR
Bibliographic record
Abstract
PURPOSE: To compare foveal avascular zone (FAZ) geometric indices using optical coherence tomography angiography (OCTA) in pneumatic retinopexy (PnR) versus pars plana vitrectomy (PPV) for rhegmatogenous retinal detachment (RRD). FAZ morphology was assessed as a possible imaging feature of retinal displacement. METHODS: This ALIGN post hoc analysis included primary fovea-off RRDs that underwent successful PnR or PPV, and performed OCTA, and fundus autofluorescence at (FAF) 3 months postoperatively at St. Michael's Hospital, Toronto, Canada. FAZ area (mm 2 ), axial ratio, circularity, and roundness were measured, and FAF images were assessed for retinal displacement. RESULTS: Seventy-two patients were included, 78% (56/72) were male mean age was 60 ± 9 years, and 60% (43/72) were phakic. Sixty-five percent (47/72) and 35% (25/72) underwent PnR and PPV, respectively. The mean baseline logarithm of the minimum angle of resolution visual acuity was 1.49 ± 0.76. FAZ circularity was lower after PPV (0.629 ± 0.120) versus PnR (0.703 ± 0.122); P = 0.016. Sixty-six patients had gradable FAF images. Retinal displacement was present in 29% (19/66), 84.2% (16/19) of which had displacement in the macula. FAZ circularity was lower in eyes with displacement in the macula (0.613 ± 0.110) versus those without displacement (0.700 ± 0.124); P = 0.015. There was a moderate negative correlation between 12-month aniseikonia and FAZ circularity(r = -0.262; P = 0.041). CONCLUSION: FAZ circularity was lower after PPV and in eyes with retinal displacement in the macula. Circularity was negatively correlated with 12-month aniseikonia scores. FAZ circularity may be another imaging feature to consider postoperatively after RRD repair.
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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 | 0.003 |
| 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".