Can Visual Acuity Be Reliably Measured at Home? Validation of Telemedicine Remote Computerised Visual Acuity Measurements
Bibliographic record
Abstract
PURPOSE: The recent pandemic has identified the need for telemedicine assessment of ophthalmology patients. A vital component of such assessment is visual acuity (VA) measurement. The aim of this study was to determine the feasibility and reliability of computerised 'at home' VA measurements using COMPlog software. METHODS: A Bland Altman method comparison study of worse eye 'in clinic' and 'at home' orthoptist-supervised COMPlog computerised VA measurements. Subjects underwent gold standard semi-automated computerised test and retest logMAR VA measurements on their habitually corrected worse eye both 'in clinic' and 'at home.' The orthoptist ran the test from the eye clinic with the patient viewing a secondary PC monitor either in the same clinic room or at home. A screen sharing voice and video conferencing application and standard consumer IT hardware were employed to present the test optotypes in the patient's home. RESULTS: 23 paediatric and 13 adult patients with a range of ocular diseases and worse eye visual acuities were included (range -0.14 to 1.06 logMAR). No significant bias was found between 'in clinic' and 'at home' measurements (mean -0.01 logMAR and 95% confidence interval -0.03, 0.00 logMAR). Test-retest variability of 'in clinic,' 'at home' and 'in clinic' versus 'at home' measurements were within normal reported ranges at 0.12 logMAR (6 ETDRS letters) or less. CONCLUSION: Remote home VA testing performed by an eye care professional using a semi-automated VA measurement program and video conferencing application provided unbiased measurements with acceptable test-retest reliability. Home testing was both feasible and acceptably reliable in appropriately equipped patients.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".