Validation of a portable, remotely delivered refraction approach compared to standard in-clinic refraction in a low-vision population
Bibliographic record
Abstract
INTRODUCTION: A low-vision assessment (LVA) is critical in developing a vision rehabilitation plan. A remotely delivered LVA that replicates a standard in-clinic assessment may bridge the gap for patients not accessing care due to the limited quantity and distribution of low-vision providers. Within an LVA, an accurate and consistent assessment of refraction error is an essential component. No system has currently been validated for the purposes of a remote LVA. The purpose of this study was to validate a commercially available portable refraction approach in a low-vision population. METHODS: = 25) underwent a refraction assessment using the Adaptica® 2WIN autorefractor, adaptor scope (Kaleidos) and VisionFit phoropter portable refraction devices, as well as a standard autorefractor (Huvitz) and phoropter (Haag-Streit). Refraction data between systems and populations were compared using intraclass correlations. Bland-Altman plots were used to assess the differences between devices. RESULTS: Spherical equivalent values were found to be reproducible between standard and experimental autorefraction devices (intraclass correlation coefficient (ICC) > 0.8) in both low-vision and normally sighted groups. Similarly, manifest refraction was highly consistent (ICC > 0.8) between devices in all groups. The Bland-Altman plots showed clinically acceptable mean differences of 0.701 between autorefraction methods and -0.116 between manifest refraction methods. DISCUSSION: The 2WIN/VisionFit system can reliably generate refraction values across a spectrum of errors in normally sighted and visually impaired people, and would be feasible to deliver remotely.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".