Measuring changes in device use of a head-mounted low vision aid after personalised telerehabilitation: protocol for a feasibility study
Bibliographic record
Abstract
INTRODUCTION: A recent trend in low vision (LV) has been towards the use of portable head-mounted displays (HMDs) to enhance residual vision. The decision process around the (non-)use of such devices have been identified as multifactorial. Among important barriers identified in the context of magnifying LV aids were transportation issues and insufficient training. In recent years, telerehabilitation has become of growing interest in healthcare because it allows individuals to remain at home while receiving rehabilitation services. A recent pilot study indicated encouraging outcomes; however, very few applications of telerehabilitation for LV have been tested systematically. METHODS AND ANALYSIS: To help guide evidence-based practice recommendations for this modality, we will carry out a feasibility study to assess the recruitment, retention, accessibility and acceptability of an eventual fully randomised trial of telerehabilitation for people with LV using HMDs. We will recruit 60 participants aged 18+ years among prospective eSight Eyewear owners, randomised 1:1 into two parallel groups. The active intervention will be the telerehabilitation operated by a LV therapist; the control arm will be the current self-training standard provided by the device vendor. The primary feasibility outcome measures will be: time to recruit participants, loss to follow-up, accessibility and acceptability of the telerehabilitation (satisfaction of the users and LV therapist). Exploratory outcomes will be the impact of telerehabilitation on eSight Eyewear use behaviour (discontinuance rate), and validated measures of assistive-technology-related quality of life. ETHICS AND DISSEMINATION: (CRIR# 1286-1217). Dissemination is planned via local, national and international healthcare conferences and peer-reviewed journal publications.
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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 | 0.070 | 0.044 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.018 |
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".