Patient Preferences with Anti-Vascular Endothelial Growth Factor Treatment for Neovascular Age-Related Macular Degeneration and Diabetic Macular Edema: A Multinational Discrete Choice Experiment Study
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Bibliographic record
Abstract
INTRODUCTION: New anti-vascular endothelial growth factor (VEGF) treatments are emerging for the treatment of diabetic macular edema (DME)/neovascular age-related macular degeneration (nAMD). This study aimed to explore the treatment attributes patients find important when deciding on treatment options. METHODS: This noninterventional survey study assessed treatment preferences through a discrete choice experiment (DCE) among patients with DME/nAMD in the USA, Canada, France, Italy, Spain, and the UK. The DCE design was informed by a targeted literature review and qualitative interview research and included five treatment attributes: mode of administration, frequency of examinations, frequency of injections or refills, likely change in visual acuity, and eye-related side effects. Conditional logit models were used to analyze the choice data. RESULTS: Overall, 537 patients completed the DCE (DME, n = 173; nAMD, n = 364). Patients reported preferring "injection" over "implant surgery and refills" and better visual outcomes over "stabilization," which were also the most important attributes driving preference (35.1% and 31.5%, respectively). They also showed a preference for less-frequent treatment and examinations and for "mild-moderate, frequent" over "severe, rare" side effects. These findings were generally consistent across the two conditions, although significant differences were found depending on anti-VEGF treatment duration (nAMD, DME) and number of reported barriers (nAMD). CONCLUSION: Patient preferences for treatment are driven by several factors. Considering these preferences is essential when designing/introducing new therapies. Individual treatment preferences should be identified and given key consideration when helping patients select from an expanding array of treatment options.
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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