P.013 Patient preferences for selection of preventive migraine therapies in Canada: results from a discrete choice experiment
Bibliographic record
Abstract
Background: This study assessed the importance of mode of administration relative to other treatment attributes when selecting a preventive migraine therapy. Methods: Cross-sectional study among Canadian adults diagnosed with migraine with ≥5 monthly migraine days and tried ≥2 prescription migraine treatments (any kind/duration). Preferences for treatments varying in the following attributes were evaluated via a discrete choice experiment: speed of efficacy (effective in 24hr/1wk/3mo), duration of efficacy (wears off never/1wk/2 wks before next dose), mode of administration (infusion/auto-injection/cranial injections), administration setting (clinic/home), and administration frequency (1mo/3mo). Attribute-level preference weights were estimated using Hierarchical Bayes modeling. Results: Of 200 respondents, 142 experienced episodic migraine and 58 experienced chronic migraine. Preference weights confirmed that respondents’ most preferred treatments were those that provided fast and long-lasting efficacy (effective in 24hr = 0.59; wears off never = 1.07) and were offered via infusion (0.58) or auto-injection (0.47) over intracranial injection (-1.04). Respondents reported being moderately willing to receive infusions in either a home or clinic setting (1-6 Likert scale from “not at all” to extremely” willing). Conclusions: Second to speed and duration of efficacy, respondents were most concerned with mode of administration when selecting their preferred migraine preventive, suggesting that physicians should consider patient preferences in treatment decision-making.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".