Optical Coherence Tomography May Be Used to Predict Visual Acuity in Patients with Macular Edema
Bibliographic record
Abstract
PURPOSE: To determine whether the volume of retinal tissue passing between the inner and outer retina in macular edema could be used as an indicator of visual acuity. METHODS: Diabetic and uveitic patients with cystoid macular edema (81 subjects, 129 eyes) were recruited. Best corrected logMAR visual acuity and spectral optical coherence tomography (OCT/SLO; OTI, Toronto, ONT, Canada) were performed in all patients. Coronal OCT scans obtained from a cross section of the retina between the plexiform layers were analyzed with a grid of five concentric radii (500, 1000, 1500, 2000, and 2500 μm centered on the fovea). The images were analyzed to determine the amount of retinal tissue present within each ring. A linear regression model was developed to determine the relationship between tissue integrity and logMAR visual acuity. RESULTS: A linear relationship between tissue integrity and VA was demonstrated. The volume of retinal tissue between the plexiform layers in rings 1 and 2 (up to 1000 μm from the foveal center) predicted 80% of visual acuity. By contrast, central macular thickness within the central 1000 μm predicted only 14% of visual acuity. CONCLUSIONS: This study showed that the cross-sectional area of retinal tissue between the plexiform layers in cystoid macular edema, as imaged by OCT, is the best indicator of visual function at baseline. Further prospective treatment trials are needed to investigate this parameter as a predictor of visual outcome after intervention.
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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".