A real‐world, observational study of erenumab for migraine prevention in Canadian patients
Bibliographic record
Abstract
OBJECTIVES: To assess real-world effectiveness, safety, and usage of erenumab in Canadian patients with episodic and chronic migraine with prior ineffective prophylactic treatments. BACKGROUND: In randomized controlled trials, erenumab demonstrated efficacy for migraine prevention in patients with ≤4 prior ineffective prophylactic migraine therapies. The "Migraine prevention with AimoviG: Informative Canadian real-world study" (MAGIC) assessed real-world effectiveness of erenumab in Canadian patients with migraine. METHODS: MAGIC was a prospective open-label, observational study conducted in Canadian patients with chronic migraine (CM) and episodic migraine (EM) with two to six categories of prior ineffective prophylactic therapies. Participants were administered 70 mg or 140 mg erenumab monthly based on physician's assessment. Migraine attacks were self-assessed using an electronic diary and patient-reported outcome questionnaires. The primary outcome was the proportion of subjects achieving ≥50% reduction in monthly migraine days (MMD) after the 3-month treatment period. RESULTS: Among the 95 participants who mostly experienced two (54.7%) or three (32.6%) prior categories of ineffective prophylactic therapies and who initiated erenumab, treatment was generally safe and well tolerated; 89/95 (93.7%) participants initiated treatment with 140 mg erenumab. At week 12, 32/95 (33.7%) participants including 17/64 (26.6%) CM and 15/32 (48.4%) EM achieved ≥50% reduction in MMD while 30/86 (34.9%) participants including 19/55 (34.5%) CM and 11/31 (35.5%) EM achieved ≥50% reduction in MMD at week 24. Through patient-reported outcome questionnaires, 62/95 (65.3%) and 45/86 (52.3%) participants reported improvement of their condition at weeks 12 and 24, respectively. Physicians observed improvement in the condition of 78/95 (82.1%) and 67/86 (77.9%) participants at weeks 12 and 24, respectively. CONCLUSION: One-third of patients with EM and CM achieved ≥50% MMD reduction after 3 months of erenumab treatment. This study provides real-world evidence of erenumab effectiveness, safety, and usage for migraine prevention in adult Canadian patients with multiple prior ineffective prophylactic treatments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".