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
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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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 | 0.034 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".