P.015 Preventive treatment with eptinezumab in patients with a dual diagnosis of chronic migraine and medication-overuse headache: subgroup analysis of PROMISE-2
Bibliographic record
Abstract
Background: This post hoc analysis of the PROMISE-2 data provides an assessment of the total preventive migraine efficacy of eptinezumab over 24 weeks in patients with a dual diagnosis of chronic migraine (CM) and medication overuse headache (MOH). Methods: PROMISE-2 was a double-blind, placebo-controlled, phase 3 study of eptinezumab (NCT02974153) over 24 weeks. Endpoints analyzed here include changes in MMDs, changes in monthly days of AHM use (total and class-specific), percentage of patients below thresholds for CM and MOH, and assessments patient-reported outcomes (PROs). Results: 40.2% patients with CM also had a diagnosis of MOH at baseline. Mean changes from baseline in MMDs during Weeks 1–12 were -8.4 and -8.6 in eptinezumab 100 mg and 300 mg treatment groups, respectively (vs 16.7 at baseline), compared with -5.4 in the placebo group (P<0.0001 vs placebo for both doses). Total monthly AHM use also decreased with eptinezumab. For all 24 weeks, 51.1% (100 mg) and 54.4% (300 mg) of eptinezumab-treated patients were below the ICHD thresholds for diagnosis of CM, compared with 32.4% of patients receiving placebo. Conclusions: This subgroup analysis of patients with a dual diagnosis of CM and MOH suggests that eptinezumab treatment resulted in greater improvements overall compared with placebo.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".