The Effect of Attention on Fixation Stability During Dynamic Fixation Testing in Stargardt Disease
Bibliographic record
Abstract
Purpose Sensitive, reproducible visual function biomarkers are necessary to evaluate the efficacy of emerging treatments for Stargardt disease type 1 in clinical trials. We previously demonstrated that fixation stability may serve as a secondary outcome parameter for visual function loss. However, the test duration and protocol have an unknown effect on the assessment of fixation stability. Here, we hypothesize that separate fixation testing with a single target is different from combined fixation testing using the same target with simultaneous perimetry testing. Design International, multicenter, prospective, cross-sectional study. Methods Microperimetry data from the international, multicenter, prospective Prog ression of Atrophy Secondary to Star gardt Disease (ProgStar, NCT01977846) study were analyzed. Patients underwent various types of fixation testing including static testing and dynamic testing, and a duration-corrected dynamic test was generated (30sEpoch). Results A total of 437 eyes from 235 patients were included (mean age, 33.8 ± 15.1 years; 55.3% female). The mean 1SD-BCEA (bivariate contour ellipse area), which is the smallest ellipse encompassing 1 standard deviation of all fixation events, was smaller for the static fixation test compared to the 30sEpoch (4.5 ± 6.9 deg 2 vs 5.3 ± 7.0 deg 2 ; P = .02) and the number of points within both the 2-degree and 4-degree circles was larger ( P < .0001). Conclusions Our results suggest that differences in static and dynamic assessment of fixation stability are dependent not only on different test durations but also on the testing protocol of a single fixation target vs fixation target plus simultaneous perimetry testing and provide information on the conduct of fixation testing 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 | 0.003 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".