P.014 Eptinezumab improved work productivity in adults with migraine and prior preventive treatment failures: results from the DELIVER study
Bibliographic record
Abstract
Background: This analysis reports the impact of eptinezumab on work productivity and daily activities in patients with migraine and prior preventive treatment failures. Methods: The DELIVER study (NCT04418765) randomized adults with migraine and documented evidence of 2-4 prior preventive treatment failures to receive eptinezumab 100mg, 300mg, or placebo (IV every 12 weeks). At baseline and every 4 weeks, patients completed the migraine-specific 6-question Work Productivity Activity Impairment (WPAI:M) questionnaire (7-day recall). Changes from baseline in WPAI subscores were predefined secondary endpoints and analyzed without control for multiplicity. Results: The full analysis set included 890 patients (100mg, n=299; 300mg, n=293; placebo, n=298). Mean baseline WPAI subscores indicated a negative impact of migraine on work productivity and normal daily activities. Beginning at first post-baseline assessment at Week 4 and through Week 24, eptinezumab demonstrated larger reductions than placebo in absenteeism (P<0.05), presenteeism (P<0.001), work productivity loss (P<0.001), and activity impairment (P<0.001) subscores. Conclusions: In adults with migraine and prior preventive treatment failures, eptinezumab treatment robustly improved migraine-related absenteeism, presenteeism, work productivity loss, and activity impairment as early as Week 4 and throughout the study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.005 | 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".