P.026 Erenumab associated with high persistence among Canadian patients for preventive treatment of chronic and episodic migraine in real-world practice
Bibliographic record
Abstract
Background: Real world use of oral prophylactic migraine therapies is often limited by poor patient tolerance. The objective of this study was to describe the demographics and clinical characteristics of patients prescribed erenumab following its launch in Canada (September 2018) and to evaluate the real-world treatment persistence and dose management. Methods: This was a retrospective, descriptive analysis of de-identified secondary patient data that includes baseline demographics, clinical characteristics, plus erenumab treatment management, collected through Novartis’ Go Program® (Patient Support Program). Only data collected from patients with a documented informed consent were included in the analysis. Results: 14,282 patients met eligibility criteria. The mean age of patients was 46.3 years, 83.0% were female, and 66.1% reported having ≥15 monthly migraine days. 52.5% were initiated on the 140 mg dose of erenumab and 59.3% of those who initiated the 70 mg dose escalated to 140 mg within 360 days. After 360 and 450 days, the KM-derived persistence was 71.0% and 63.4%, respectively. Conclusions: The high persistence reported here suggests that erenumab has a meaningful degree of tolerability in the real-world setting and increases confidence that the real-world use and benefits of erenumab will not be undermined by the poor persistence observed with traditional migraine prophylactic agents.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".