Fundus autofluorescence features in the inflammatory maculopathies
Bibliographic record
Abstract
PURPOSE: To describe the fundus autofluorescence (FAF) features of the inflammatory maculopathies and develop a quantification method for FAF analysis. METHODS: This is a retrospective, consecutive case series of patients with inflammatory maculopathies from two tertiary centers. The clinical findings, demographics, and FAF imaging characteristics were reviewed. Foveal autofluorescence (AF) was analyzed. Median and standard deviation (SD) of foveal AF intensity were measured. RESULTS: Thirty eyes of 15 patients were evaluated with both qualitative and quantitative FAF analysis. In acute macular neuroretinopathy, the active phase showed foveal hypoautofluorescence, which became hypoautofluorescent with resolution. In acute posterior multifocal placoid pigment epitheliopathy, multiple lesions with hypoautofluorescent centers with hyperautofluorescent borders were observed in active disease and became hypoautofluorescent with disease convalescence. In multifocal choroiditis and punctate inner choroiditis, the active hyperautofluorescent lesions progressed to inactive, hypoautofluorescent scars. Active serpiginous choroiditis showed hyperautofluorescent borders adjacent to a helicoid-shaped, hypoautofluorescent scar. Active unilateral acute idiopathic maculopathy (UAIM) showed a complex pattern of hypo- and hyperautoflourescence in the macula. The median foveal AF was the greatest in acute macular neuroretinopathy and UAIM among the maculopathies, while the greatest SD of foveal AF intensity was observed in UAIM. CONCLUSION: The active phase of the majority of inflammatory maculopathies was characterized by hyperautofluorescent lesions. Increased SD of foveal AF correlated with a mixture of hypo-and hyperautoflourescence. Median and SD may be useful metrics in foveal AF and quantifiable values that may be assessed over time as a disease process evolves. Improvements in quantification methods of FAF imaging may allow us to objectively evaluate posterior uveitis.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".