The Experience of a Randomized Clinical Trial of Closed-Circuit Television versus Eccentric Viewing Training for People with Age-Related Macular Degeneration
Bibliographic record
Abstract
Introduction In addition to optical devices, closed-circuit televisions (CCTVs) and eccentric viewing training are both recognized interventions to improve reading performance in individuals with vision loss secondary to age-related macular degeneration. Both are relatively expensive, however, either in the cost of the device or in the amount of time personnel need to provide training. In this randomized trial, we compared the effectiveness of these two interventions. Methods Participants with age-related macular degeneration and visual acuity between 6/48 (20/160) and 6/120 (20/400) first received basic low vision care, including optical devices. At the subsequent baseline visit, they undertook a battery of measures including logMAR visual acuity; reading speed and accuracy for text in 1.3M and 1M fonts; reading information on medicine bottles, utility bills, and food packages; the NEI-VFQ; the Geriatric Depression Scale; and a reading inventory questionnaire. They were then randomized to either obtaining a CCTV for home use or eccentric viewing training over the following six weeks. Results Recruitment was more difficult than expected for this population. Of 145 patients referred, 29 met the inclusion-exclusion criteria, 14 were willing to enroll, and 10 completed the trial. For the primary outcome (reading speed for 1.3M print), there was a significant improvement between baseline and outcome for the CCTV group (p = 0.005), but not for the eccentric viewing training group (p = 0.28), and the CCTV group showed significantly greater change (p = 0.04). There was a nonsignificant improvement in reading speed for 1M text and a decrease in the amount of time taken to read utility bill information in the CCTV group. There was a significant improvement in near visual acuity with current glasses with eccentric viewing training. The other measures did not reach statistical significance. Discussion Randomized clinical trials for low vision rehabilitation, particularly in the elderly population with vision loss, are challenging, but such trials are important for the allocation of resources. This trial showed early indications of more impact on reading performance from CCTV than eccentric viewing training.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".