Remote vision testing of central retinal acuity and comparison with clinic-based Snellen acuity testing in patients followed for retinal conditions
Bibliographic record
Abstract
Introduction: The unmet need for remote monitoring of visual function with home-based, patient-centric technologies became increasingly palpable during the COVID-19 pandemic. Many patients with chronic eye conditions lack access to office-based examinations. Here, we evaluate the efficacy of the Accustat® test, a virtual application for measuring near visual acuity on any portable electronic device via telehealth. Materials and methods: Thirty-three adult subjects from the telehealth remote monitoring service of a retina practice performed the Accustat® acuity testing at home. All patients underwent in-office general eye examination with additional fundoscopic examination and optical coherence tomography retina imaging. Best corrected visual acuity assessment using a Snellen chart was compared with remote visual acuity assessment with the Accustat® test. Visual acuity was analyzed and compared between the best-corrected near visual acuity potential achieved on the Accustat® and in-office distance best-corrected Snellen visual acuity. Results: The mean logarithm of the minimum angle of resolution (logMAR) visual acuities of all eyes tested using the Accustat test was 0.19 ± 024 and for the office Snellen test 0.21 ± 0.21. A linear regression model with 95% confidence intervals reveals that there is a strong linear relationship between Accustat logMAR and office Snellen logMAR. Bland-Altman analysis demonstrated 95.2% significant agreement between Accustat and Office Snellen's best corrected visual acuity. Intraclass correlation coefficient (ICC = 0.94) demonstrated a strong positive correlation between at home versus office visual acuity. Conclusion: There was a high correlation between the visual acuity measured with the Accustat near vision digital self-test and the office Snellen acuity test, suggesting the potential utility of scalable remote monitoring of central retinal function via telehealth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| 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".