Assessment of patient education materials for age‐related macular degeneration
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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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".