P.016 Impact of eptinezumab on patient-reported outcomes in patients with prior preventive treatment failures
Bibliographic record
Abstract
Background: In DELIVER, eptinezumab reduced migraine frequency and was well tolerated in patients with migraine and prior preventive treatment failures. This analysis evaluated changes in patient-reported outcomes (PROs). Methods: DELIVER (NCT04418765; phase 3b double-blind study) randomized adults with migraine and 2-4 prior preventive treatment failures to eptinezumab or placebo every 12 weeks. The assessed PROs included EuroQol 5-Dimensions 5-Levels visual analogue scale (EQ-5D-5L VAS); 6-item Headache Impact Test (HIT-6), Patient Global Impression of Change (PGIC), most bothersome symptom (MBS), and Migraine-Specific Quality of Life Questionnaire (MSQ, v2.1). Results: Patients received eptinezumab 100mg (n=299), 300mg (n=294), or placebo (n=298). Mean changes from baseline to Wk12 in EQ-5D-5L VAS scores were 2.0 (100mg, P=0.0007) and 4.4 (300mg, P<0.0001) versus -3.1 (placebo), and were maintained or further improved to Wk24 (2.0, 5.2, -2.8, respectively). Mean baseline HIT-6 total scores were ~66.4, with mean changes of -6.9 (100mg, P<0.0001) and -8.5 (300mg, P<0.0001) versus -3.1 (placebo) at Wk12 that were further improved through Wk24 (-8.9 and -9.9 vs -3.9). PGIC, MBS, and MSQ domain scores showed greater improvement for eptinezumab than placebo. Conclusions: In adults with migraine and prior preventive treatment failures, eptinezumab robustly improved health-related quality of life and migraine-related burden over 24 weeks versus 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".