The Effects of Low-Vision Rehabilitation on Reading Speed and Depression in Age Related Macular Degeneration: A Meta-Analysis
Bibliographic record
Abstract
IMPORTANCE: Age related macular degeneration (AMD) is a progressive eye disease that, as of 2015, has affected 11 million people in the U.S. and 1.5 million in Canada causing central vision blindness. By 2050, this number is expected to double to 22 million. Eccentric vision is the target of low-vision rehabilitation aids and programs for patients with AMD, which are thought to improve functional performance by improving reading speed and depression. OBJECTIVE: This study evaluates the effect of various low-vision rehabilitation strategies on reading speed and depression in patients 55 and older with AMD. DATA SOURCES: Computer databases including MEDLINE (OVID), EMBASE (OVID), BIOSIS Previews (Thomson-Reuters), CINAHL (EBSCO), Health Economic Evaluations Database (HEED), ISI Web of Science (Thomson-Reuters) and the Cochrane Library (Wiley) were searched from the year 2000 to January 2015. STUDY SELECTION: Included papers were research studies with a sample size of 20 eyes or greater focused on AMD in adults aged 55 or older with low vision (20/60 or lower). DATA EXTRACTION AND SYNTHESIS: Two independent reviewers screened and extracted relevant data from the included articles. Standardized mean difference (SMD) was chosen as an effect size to perform meta-analysis using STATA. Fixed- and random-effect models were developed based on heterogeneity. MAIN OUTCOMES: Reading Speed and Depression Scores. RESULTS: A total of 9 studies (885 subjects) were included. Overall, a significant improvement in reading speed was found with a SMD of 1.01 [95% CI: 0.05 to 1.97]. Low-vision rehabilitation strategies including micro-perimetric biofeedback, microscopes teaching program significantly improved reading speed. Eccentric viewing training showed the maximum improvement in reading speed. In addition, a non-significant improvement in depression scores was found with a SMD of -0.44 [95% CI: -0.96 to 0.09]. CONCLUSION: A considerable amount of research is required in the area of low-vision rehabilitation strategies for patients with AMD. Based on current research, low-vision rehabilitation aids improve reading speed. However, they do not have a significant effect on depression scores in those 55 and older with AMD.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.060 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".