Remote patient monitoring of central retinal function with MACUSTAT <sup>®</sup> : A multi-modal macular function scan
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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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 | 0.000 | 0.001 |
| 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) | 0.003 | 0.001 |
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".