The Effect of a Head‐mounted Low Vision Device on Visual Function
Bibliographic record
Abstract
SIGNIFICANCE: Head-mounted low vision devices have received considerable attention in recent years owing to rapidly developing technology, facilitating ease of use and functionality. Systematic clinical evaluations of such devices remain rare but are needed to steer future device development. PURPOSE: The purpose of this study was to investigate, in a multicenter prospective trial, the short- and medium-term effects of a head-worn vision enhancement device (eSight Eyewear). METHODS: Participants aged 13 to 75 years with stable vision (distance acuity, 20/60 to 20/400; visual field diameter >20°) were recruited across six sites. Data were collected at baseline (no device), at fitting (with device), and after 3 months of everyday use. Outcome measures were visual ability measured by the Veterans Affairs Low Vision Visual Functioning Questionnaire 48, distance acuity (Early Treatment Diabetic Retinopathy Study), reading performance (MNREAD chart), contrast sensitivity (MARS chart), face recognition, and a modified version of the Melbourne Low Vision Activities of Daily Living (ADL) Index. RESULTS: Among the 51 participants, eSight introduction immediately improved distance acuity (0.74 ± 0.28 logMAR), contrast sensitivity (0.57 ± 0.53 log units), and critical print size (0.52 ± 0.43 logMAR), all P < .001, without any further change after 3 months; reading acuity improved at fitting (0.56 ± 0.35 logMAR) and by one additional line after 3 months, whereas reading speed only slightly increased across all three time points. The Melbourne ADL score and face recognition improved at fitting (P < .01) with trends toward further improvement at 3 months. After 3 months of use, Veterans Affairs Low Vision Visual Functioning Questionnaire 48 person measures (in logits) improved: overall, 0.84, P < .001; reading, 2.75, P < .001; mobility, 0.04, not statistically significant; visual information, 1.08, P < .001; and visual motor, 0.48, P = .02. CONCLUSIONS: eSight introduction yields immediate improvements in visual ability, with face recognition and ADLs showing a tentative benefit of further use. Overall, visual ability, reading, and visual information showed greatest benefit with device use. Further studies need to examine benefits of practice and training and possible differential effects of underlying pathology or baseline 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".