Remote patient monitoring of central retinal function with MACUSTAT <sup>®</sup> : A multi-modal macular function scan
Why this work is in the frame
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Bibliographic record
Abstract
Introduction: There is significant unmet need for patient-centric remote monitoring of visual function for chronic retinal diseases, as demonstrated by the COVID-19 pandemic. The Macustat® central retinal function scan is a novel cloud-based digital health application for remote monitoring. The aim of this study is to assess the efficacy of the Macustat® compared to traditional in-office retinal evaluations. Materials and methods: Patients with underlying macular pathology underwent office-based retinal and visual acuity examinations and OCT macula imaging followed by remote tele-monitoring assessment with the Macustat. Central visual function was assessed with the multi-modal Macustat test using dynamic virtual Amsler grid testing, hyperacuity perimetry and visual acuity testing. The results were compared to the findings of the in-office comprehensive retina exam and OCT evaluation. Results: The foveal acuity potential registered with the Macustat test showed high correlation with the office Snellen acuity potential 96% of eyes registered Macustat acuity within 0.2 LogMAR of office acuity measurement. In Wet AMD eyes with CNV pathology documented on OCT, the Macustat foveal function scan showed a corresponding abnormality in 89% of any CNV eyes and 100% of all visually significant CNV. In normal eyes without any visually significant edema or CNV, more than 92% showed corresponding normal retinal function scan. Conclusion: The Macustat demonstrates high concordance with clinical findings using traditional diagnostic devices. Home monitoring with the Macustat® may offer complementary clinical utility as a telehealth tool for the assessment of visual acuity and macular function in patients at high risk for macular disease.
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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 | 0.000 |
| 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) | 0.000 | 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