The Progression of Stargardt Disease Using Volumetric Hill of Vision Analyses Over 24 Months: ProgStar Report No.15
Bibliographic record
Abstract
Purpose To report the yearly rate of change in macular function in patients with Stargardt disease type 1 (STGD1) over 24 months and to establish a new volumetric visual function index for use in clinical trials investigating the efficacy on retinal sensitivity. Methods Design : International, multicenter, prospective cohort study with 5 study visits every 6 months over 24 months. Participants : A total of 233 individuals with genetically confirmed STGD1 (≥1 disease-causing ABCA4 variant). Main Outcome Measures: The total volume (V TOT ) beneath the sensitivity surface of a 3-D model of the hill of vision and mean sensitivity (MS) derived from mesopic microperimetry performed with a white stimulus. Changes of V TOT over time and its correlation with the ABCA4 genotype and baseline features. Results At baseline, 440 eyes (233 patients) with a mean (SD) age of 33.7 (15.0) years, mean (SD) visual acuity of 46.08 (16.03) ETDRS letters were analyzed with an average V TOT of 0.91 decibel-steradian (dB-sr) and an MS of 10.73 dB. The overall mean rate of decrease in sensitivity [95% confidence interval] was 0.077 [0.064, 0.090] dB-sr/y for V TOT and 0.87 [0.72, 1.02] dB/year for MS. The progression rate of V TOT depended on baseline visual function (0.029 dB-sr/year for low and 0.120 dB-sr/year for high baseline V TOT ; P < .001) and exhibited a difference in the first vs second year of follow-up (0.065 dB-sr/year vs 0.089 dB-sr/year, respectively; P < .001). The absence of pigmentary abnormalities of the retinal pigment epithelium at baseline was found to be associated with a faster progression rate ( P < .001), whereas a significant association with the genotype was not detected ( P = .7). Conclusion In STGD1, both microperimetric outcomes demonstrate statistically significant and clinically meaningful changes after relatively short follow-up periods. Volumetric modeling may be useful in future interventional clinical trials that aim to improve retinal sensitivity or to slow down its decline and for structure-function correlations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".