Comparing the Netra smartphone refractor to subjective refraction
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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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 | 0.002 | 0.006 |
| 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) | 0.003 | 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".