Patient preferences for preventive migraine treatments among Canadian adults: A discrete choice experiment
Bibliographic record
Abstract
OBJECTIVE: To evaluate preferences for key attributes of injected or infused preventive migraine treatments and assess heterogeneity in preferences among Canadian participants with migraine. BACKGROUND: Current treatment options for migraine prevention differ in their attributes, including mode of administration, efficacy, and dosing frequency; preferences for such attributes can vary among patients. With the advent of new therapies, evidence demonstrating patient preferences for injected or infused preventive migraine treatments is necessary. METHODS: Canadian adults self-reporting a diagnosis of migraine completed a cross-sectional, internet-based survey that included a discrete choice experiment. Participants were presented with attributes of preventive migraine treatments, including speed of onset, durability of efficacy, mode of administration, administration setting, and dosing frequency. Latent class analysis (LCA) was used to identify subgroups of patients who differed in their treatment preferences. RESULTS: In total, 200 participants completed the survey. Participants' treatment preferences were most sensitive to improvements in the durability of effectiveness from "wears off 2 weeks before next dose" to "does not wear off before the next dose" (absolute difference in weights = |-0.95 to 1.07| = 2.02) and improvements from "cranial injections" to "intravenous infusions" (|-1.04 to 0.58| = 1.62); participants equally preferred self-injection and intravenous infusion from a health-care provider (mean weight = 0.58 and 0.47, respectively) as a route of administration over cranial injections (mean weight = -1.04). Three subgroups were identified with LCA: group one (n = 103) prioritized fast-acting and durable therapies, group two (n = 54) expressed aversion to cranial injections, and group three (n = 43) favored treatments administered in a health-care provider setting. CONCLUSIONS: In this sample of Canadian adults with migraine, we showed that durability of effectiveness and mode of administration are key attributes influencing patient preferences for preventive migraine treatments; however, certain groups of patients may differ in their treatment priorities. Our results highlight the need for patient-provider discussions regarding treatment attributes and consideration of patients' preferences when selecting a preventive migraine treatment.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".