Validation of the visual acuity iPad app Eye Chart Pro compared to the standard Early Treatment Diabetic Retinopathy Study chart in a low-vision population
Bibliographic record
Abstract
Introduction A low-vision assessment (LVA) is central to developing a vision rehabilitation plan. However, access to LVAs is often limited by the quantity and geographic distribution of low-vision providers, as well as patient-centred transportation challenges. A tablet-based LVA tool kit, delivered virtually, has the potential to overcome many of these barriers. The purpose of this research was to validate a key component of the tablet-based tool kit – a commercially available iPad visual acuity (VA) test (Eye Chart Pro) iPad app – in a low-vision population. Methods Participants with low vision ( n = 26) and those who were normally sighted ( n = 25) underwent VA testing with both the iPad VA test application and the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The VA data were compared using a t-test, linear regression and Bland–Altman analysis. Results There was no significant difference in the mean absolute difference in VA (log of minimum angle of resolution (logMAR)=0.11; p = 0.82). Eye Chart Pro and Standard ETDRS Chart measures were also not significantly different ( p = 0.98). However, there were significant differences between test methods in the low-vision group and the normally sighted group ( p > 0.0001 and p = 0.007, respectively). The Bland–Altman analysis showed a mean bias (difference) of –0.0005 logMAR between methods, and 95% limits of agreement of 0.298 and –0.299 logMAR. Discussion The ETDRS chart function on the Eye Chart Pro application can reliably measure VA across a range, from normally sighted patients to those with low vision.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".