Assessment of patient education materials for age‐related macular degeneration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Age-related macular degeneration (AMD) is a leading cause of vision loss. It is helpful for patients living with AMD to understand the prognosis, risk factors and management of their condition. Online education materials are a popular and promising channel for conveying this knowledge to patients with AMD. However, the quality of these materials-particularly with respect to qualities such as 'understandability' and 'actionability'-is not yet known. This study assessed a collection of online materials about AMD based on these qualities of 'understandability' and 'actionability'. METHODS: Online education materials about AMD were sourced through Google from six English-speaking nations: Australia, New Zealand, USA, UK, Ireland and Canada. Three Australian/New Zealand trained and registered optometrists participated in the grading of the 'understandability' and 'actionability' of online education materials using the Patient Education Materials Assessment Tool (PEMAT). RESULTS: This study analysed a total of 75 online materials. The mean 'understandability' score was 74% (range: 38%-94%). The 'understandability' PEMAT criterion U11 (calling for a summary of the key points) scored most poorly across all materials. The mean 'actionability' score was 49% (range: 0%-83%). The 'actionability' PEMAT criterion A26 (using 'visual aids' to make instructions easier to act on) scored most poorly across all materials. CONCLUSION: Most education materials about AMD are easy to understand, but difficult to act on, because of a lack of meaningful visual aids. We propose future enhancements to AMD education materials-including the use of summaries, visual aids and a habit tracker-to help patients with AMD improve their understanding of disease prognosis, risk factors and eye assessment schedule requirements.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it