Calcitonin Gene-Related Peptide Inhibitor Use in 2018–2023: A Retrospective Cohort Study Across Six Canadian Provinces
Bibliographic record
Abstract
ABSTRACT Background: A better understanding of calcitonin gene-related peptide (CGRP) inhibitor use is in migraine treatment needed. Methods: A retrospective, observational, population-based cohort study was conducted using administrative data. Adults (≥18 years) who received ≥1 prophylactic CGRP inhibitor in Canada (six provinces) between 2018 (first approved) and 2023 were identified. CGRP inhibitor use was described; migraine-related acute medication and healthcare use were compared pre–post CGRP inhibitor initiation (independent and paired t -tests). Results: 12,851 adults were identified. CGRP inhibitor use increased from 11.8 (incident/prevalent) to 22.4 (incident) and 57.3 (prevalent) per 100,000 adults. Erenumab use decreased over time, as use of newer agents increased. During the 1-year period after CGRP inhibitor initiation, 57.4% had concomitant use with a different prophylactic migraine medication class (onabotulinumtoxinA injection: 23.2%; oral non-CGRP inhibitor: 34.2%), and 30.4% stopped use (21.3% switched to a different prophylactic migraine medication class; 9.1% discontinued all prophylactic migraine medication). During the 1-year period after CGRP inhibitor initiation (versus before), days of supply for migraine-related acute medication was lower (mean [standard deviation]: 129 [191] versus 145 [197] days; mean difference [95% confidence interval]: −16: [−22, −11] days), as were the number of healthcare visits (7.36 [8.70] versus 9.18 [10.10]; −1.82 [−2.06, −1.58]). Conclusion: CGRP inhibitor use increased from 2018 to 2023. After CGRP inhibitor initiation, most patients had concomitant use with a different prophylactic migraine medication class, and some stopped use; migraine-related acute medication and healthcare use were lower (versus before). Findings provide a real-world description of the evolving landscape of CGRP inhibitor use in Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".