Effectiveness of assistive devices in improving reading performance in vision rehabilitation patients
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of assistive devices in improving the reading performance of patients in a vision rehabilitation clinic. METHODS: This is a prospective observational cohort study of patients referred to a hospital-based, urban, vision rehabilitation clinic in Southeastern Ontario. Patient demographics, ophthalmic diagnoses, and reading performance were investigated. Median reading speeds in words per minute (wpm) were compared with and without assistive devices, using the Minnesota Reading tool (MNRead). The relationships between improvement in reading speed and MNRead components (i.e., reading acuity, critical print size, and reading accessibility index), visual acuity, age, level of education, and previous exposure to vision rehabilitation were assessed. Subgroup analyses of patients diagnosed with age-related macular degeneration (AMD) and glaucoma were carried out. RESULTS: A total of 199 patients were included in this study, with 71.9% over the age of 65. Most self-identified as female (68.8%) and Caucasian (93.0%). The median improvement in reading speed with assistive devices was 39.5 wpm (P < 0.001). Improvement in reading speeds was positively correlated with visual acuity (P = 0.005) and inversely correlated with patients' age (P = 0.029). Patients with glaucoma experienced a more substantial increase in reading speed (66.2 wpm) with assistive devices compared to AMD (38.0 wpm). Handheld magnifiers were significantly more effective in AMD patients, as compared to glaucoma. CONCLUSIONS: This study provides an overview of a group of patients accessing vision rehabilitation interventions. We use the MNRead-based reading speed assessment to demonstrate the varying effectiveness of assistive devices across different patient groups.
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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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".