Comparing the Netra smartphone refractor to subjective refraction
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Among technologies targeting mobile eye care, EyeNetra is a smartphone-based subjective refraction system. This study compared the results from this system with those of professional subjective refraction. Participant visual comfort and preference of results were also measured. METHODS: Thirty-six optometry-naïve participants (n = 36 eyes, aged 18-35 years), were randomly subjected to three refraction methods: professional subjective refraction, unassisted Netra (participants alone) and refined Netra (sphere results refined by a practitioner). Using a randomised, double-blind design, refraction results were mounted in a trial frame and distance logMAR visual acuities were measured. Subjective appreciation and visual comfort were assessed by questionnaire. Overall preference was ranked. RESULTS: Unassisted Netra yielded a median myopic overcorrection of 0.60 D (interquartile range [IQR] 0.25 to 0.94) compared to professional subjective refraction. Median equivalent sphere with unassisted Netra (-1.40 D, IQR -3.10 to -0.90) was significantly more myopic than refined Netra (-0.70 D, IQR -1.60 to -0.30) and then subjective refraction (-0.80 D, IQR -1.60 to -0.30) (all p-values < 0.01). Median visual acuity with professional subjective refraction (-0.16, IQR -0.22 to -0.09) was superior than unassisted Netra (-0.08, IQR -0.20 to 0.03) (p < 0.01). Subjective refraction was ranked first in preference of trial framed results by 72 per cent of participants; median preference rank favoured professional subjective refraction to both Netra results (all p < 0.01). For all questionnaire items, visual comfort was higher with subjective refraction than with unassisted Netra (all p < 0.04). CONCLUSION: The Netra device - especially when used without professional assistance and compared to subjective refraction - induces significant myopic overcorrection and lower levels of visual acuity, subjective preference and visual comfort.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it