Visual Acuity, Full-field Stimulus Thresholds, and Electroretinography for 4 Years in The Rate of Progression of USH2A-related Retinal Degeneration (RUSH2A) Study
Bibliographic record
Abstract
Purpose To describe progression of best-corrected visual acuity (BCVA), full-field stimulus thresholds (FST), and electroretinography (ERG) over 4 years in the RUSH2A study and to assess their suitability as clinical trial endpoints. Design Prospective natural history study. Participants Participants (n=105) with biallelic disease-causing sequence variants in USH2A and BCVA letter score of ≥54 were included. Methods BCVA, FST, fundus-guided microperimetry, static perimetry and spectral domain optical coherence tomography were performed annually and ERG at baseline and 4 years only. Mixed effects models were used to estimate annual rates of change with 95% confidence intervals (CI). Associations of change from baseline to 4 years between BCVA, FST, ERG and other metrics were assessed with Spearman correlation coefficients (r s ). Main Outcome Measures BCVA, FST, ERG. Results The annual rate of decline in BCVA was 0.83 (95% CI: 0.65, 1.02) letters/year. For FST, the change was 0.09 (0.07, 0.11) log cd.s/m 2 /year for white threshold, 0.10 (0.08, 0.12) log cd.s/m 2 /year for blue threshold and 0.05 (0.04, 0.06) log cd.s/m 2 /year for red threshold. Changes were 22.6 (17.4, 28.2) %/year for white threshold, 26.0 (20.3, 32.1) %/year for blue threshold, and 12.3 (8.7, 16.0) %/year for red threshold. The high percentage of eyes with undetectable ERGs at baseline limited assessment of change. Conclusion BCVA was not a sensitive measure of progression over 4 years. FST was a more sensitive measure; however, additional information on the clinical relevance of changes in FST is needed before this test can be adopted as an endpoint for clinical trials.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".