P.033 Eptinezumab reduced acute medication use in patients with chronic migraine and medication-overuse headache: subgroup analysis of Promise-2
Bibliographic record
Abstract
Background: Eptinezumab is a preventive migraine treatment approved in the US. We evaluated the impact of eptinezumab on acute headache medication (AHM) use in patients diagnosed with chronic migraine (CM) and medication-overuse headache (MOH) in PROMISE-2. Methods: PROMISE-2 randomized patients with CM to eptinezumab 100mg, 300mg, or placebo for 2 intravenous doses administered every 12 weeks. Trained investigators diagnosed MOH at screening using 3-month medication history and ICHD-3b criteria. Endpoints included days/month of any AHM use (days of ≥1 medication class), total AHM use (summed days for each medication class), and triptan use over Weeks 1-12 and 13-24. AHM classes included triptan, ergot, opioid, simple analgesic, and combination analgesic. Results: Of 1072 PROMISE-2 patients, 431 (40.2%) were diagnosed with MOH (100mg, n=139; 300mg, n=147; placebo, n=145). During the 28-day baseline period, mean days of any AHM was ~16.4, total AHM was ~20.4, and triptan was ~8.9 across treatment arms. Over Weeks 1-12, mean days/month of any AHM was 8.8 (100mg), 9.9 (300mg), and 11.8 (placebo); total AHM was 10.8, 12.2, and 14.8; triptan was 4.3, 4.4, and 6.4. Similar or lower rates were observed over Weeks 13-24. Conclusions: In patients diagnosed with both CM and MOH, eptinezumab treatment reduced AHM use.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".