Faster Sensitivity Loss around Dense Scotomas than for Overall Macular Sensitivity in Stargardt Disease: ProgStar Report No. 14
Bibliographic record
Abstract
PURPOSE: Mean sensitivity (MS) derived from a standard test grid using microperimetry is a sensitive outcome measure in clinical trials investigating new treatments for degenerative retinal diseases. This study hypothesizes that the functional decline is faster at the edge of the dense scotoma (eMS) than by using the overall MS. DESIGN: Multicenter, international, prospective cohort study: ProgStar Study. METHODS: Stargardt disease type 1 patients (carrying at least 1 mutation in the ABCA4 gene) were followed over 12 months using microperimetry with a Humphrey 10-2 test grid. Customized software was developed to automatically define and selectively follow the test points directly adjacent to the dense scotoma points and to calculate their mean sensitivity (eMS). RESULTS: Among 361 eyes (185 patients), the mean age was 32.9 ± 15.1 years old. At baseline, MS was 10.4 ± 5.2 dB (n = 361), and the eMS was 9.3 ± 3.3 dB (n = 335). The yearly progression rate of MS (1.5 ± 2.1 dB/year) was significantly lower (β = -1.33; P < .001) than that for eMS (2.9 ± 2.9 dB/year). There were no differences between progression rates using automated grading and those using manual grading (β = .09; P = .461). CONCLUSIONS: In Stargardt disease type 1, macular sensitivity declines significantly faster at the edge of the dense scotoma than in the overall test grid. An automated, time-efficient approach for extracting and grading eMS is possible and appears valid. Thus, eMS offers a valuable tool and sensitive outcome parameter with which to follow Stargardt patients in clinical trials, allowing clinical trial designs with shorter duration and/or smaller cohorts.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".