Patient Preferences in the Management of Wet Age-Related Macular Degeneration: A Conjoint Analysis
Bibliographic record
Abstract
INTRODUCTION: To identify patient preference drivers related to the management of wet age-related macular degeneration (wet AMD). METHODS: In this cross-sectional study, a self-explicated 'conjoint analysis' survey was administered online to eligible patients with wet AMD (receiving anti-vascular endothelial growth factor [VEGF] treatment for at least 12 months) from the USA, Canada, UK, France, Spain, Germany, Italy, Japan, Taiwan, and Australia. The survey consisted of six domains with 21 attributes, which were selected on the basis of a literature review, social media listening, and tele-interviews/discussions with patients, clinical experts, and patient groups. Utility and relative importance scores were generated for each attribute and utility difference significance testing was performed using 'unequal variances t tests'. The Patient Activation Measure (PAM-13) questionnaire was administered to assess patients' knowledge, skill, and confidence in self-management. RESULTS: A total of 466 patients (mean age, 68 years; women, 54%; binocular wet AMD, 28%) with an average anti-VEGF treatment duration of 3.9 years completed the survey. The most important preference domains were 'treatment effects on vision' (non-significant) and 'vision-related symptom burdens' (p < 0.001), followed by 'treatment risk' (p < 0.05), 'impact on daily activities' (p < 0.05), 'burden of clinic/hospital visits' (p < 0.001), and 'impact on psychological well-being'. The five most important attributes in order of importance were clarity of vision, treatment effect on symptoms, quality of vision, time to treatment effect, and time to re-administration. The two most important attributes globally were also in the top three attributes across countries. The majority of participants in the study were level 3 or level 4 of the PAM-13 questionnaire. CONCLUSIONS: This study identified the most important disease and treatment attributes to patients using patient-centred methods. The data showed the degree of harmonization of preferences across geographies and that participants actively adopt behaviours required for improved treatment outcomes. The identified preference drivers may inform future clinical development.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".