Impact of eptinezumab on work productivity beyond reductions in monthly migraine days: post hoc analysis of the DELIVER trial
Bibliographic record
Abstract
BACKGROUND: Eptinezumab's impact on self-reported work productivity in adults with migraine and 2‒4 prior preventive migraine treatment failures is not fully understood. METHODOLOGY: Electronic diaries captured monthly migraine days (MMDs) reported by patients enrolled in the randomized, double-blind, placebo-controlled DELIVER trial (NCT04418765). The migraine-specific Work Productivity and Activity Impairment questionnaire, administered at baseline and each monthly visit, was a secondary outcome of DELIVER and used to model changes from baseline in self-reported monthly hours of absenteeism (decreased work attendance) and presenteeism (reduced work efficiency while at work with a migraine) in Canada, as the base case. Path analysis illustrated eptinezumab's impact on work productivity beyond MMDs. RESULTS: As MMDs increased, monthly hours of absenteeism increased linearly while those of presenteeism increased quadratically. Best-fit models were improved after including an eptinezumab treatment effect, showing benefit over placebo after controlling for MMD frequency. Compared to placebo, patients treated with eptinezumab (pooled) had a modeled reduction from baseline of 7.2 h/month (absenteeism) (95% CI: -9.9, -4.5; P < 0.001) and 21.4 h/month (presenteeism) (95% CI: -26.3, -16.5; P < 0.001) over weeks 1‒24. Beyond MMD reductions, improvements in patient-identified most bothersome symptom (PI-MBS) and reductions in percent of severe migraine attacks contributed to eptinezumab's effect. CONCLUSIONS: Eptinezumab decreased absenteeism and presenteeism based on patient reports, with data highlighting the importance of considering the PI-MBS. The greater change from baseline than placebo in self-reported absenteeism and presenteeism was only partly explained by changes in MMDs, presenting a potential opportunity to decrease the cost of migraine in the workplace. TRIAL REGISTRATION: ClinicalTrials.gov (Identifier: NCT04418765); EudraCT (Identifier: 2019-004497-25).
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".