Quantifying the Progression of Stargardt Disease in Double-Null ABCA4 Carriers Using Fundus Autofluorescence Imaging
Why this work is in the frame
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Bibliographic record
Abstract
Purpose: To score real-world fundus autofluorescence (FAF) images of pediatric patients with ABCA4-related Stargardt disease (STGD1), in a way that is automatable, scales with the disease progression, and is applicable to a wide time interval in the natural history of the disease. Methods: We developed the score based on a series of Optos wide-field FAF images of pediatric STGD1 patients (73 images; 14 individuals) and controls (27 images; 8 individuals). The patients' images were obtained over up to 6 years, and the controls over up to 5 years. In each image, we manually selected an artifact-free region, within which we evaluated an average of the pixel-level intensity score, constructed so that the average increases with progression of the disease. Results: The score we propose provides a statistically robust measure of disease progression (91% Spearman correlation with the absolute age, 97% with the estimated time from onset, when averaged over both eyes), comparable across timepoints and patients. Conclusions: FAF is a reliable tool in STGD1 diagnostics, but its quantitative description must be modified to be applicable to tracking the disease progression. Analyzing images obtained in the course of clinical care of pediatric patients poses special challenges that make complete automation difficult. Translational Relevance: Our methodology provides a quantitative tool for investigating the natural progression of the Stargardt disease, and, potentially, the effects of genotype, environment, and therapeutic intervention on its course.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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