P.017 Optimization of acute treatment and headache-related impact following eptinezumab initiated during a migraine attack: post hoc analysis of the RELIEF study
Bibliographic record
Abstract
Background: Patients administered eptinezumab during an active migraine had larger numerical improvement in the 6-item Migraine Treatment Optimization Questionnaire (mTOQ-6) total score compared to placebo. The mTOQ-6 was used to determine success of acute treatment. Methods: RELIEF (NCT04152083) was a double-blind trial that randomized adults eligible for preventive migraine treatment to eptinezumab 100mg or placebo, administered intravenously within 1–6 hours of migraine onset. mTOQ-6 was captured at baseline and Week 4 and rescored into mTOQ-4. Patients were grouped by baseline mTOQ-4 total scores. Results: 226 eptinezumab-treated and 232 placebo patients were included. The percentage of patients in the combined very poor and poor optimization subgroups at baseline with eptinezumab (n=155; 68.6%) versus placebo (n=138; 59.5%) decreased by 26.6 percentage points (n=95; 42.0%) and 9.9 percentage points (n=115; 49.6%), respectively, at Week 4. Of the 155 eptinezumab-treated and 138 placebo patients who were very poorly/poorly optimized at baseline, 73 (47.1%) versus 35 (25.4%) were moderately/maximally optimized at Week 4, respectively. Greater improvements in mTOQ-6 scores were noted in patients more poorly optimized at baseline than those more optimized. Conclusions: Eptinezumab showed greater acute migraine medication optimization and decreased headache-related impact compared to placebo, suggesting that eptinezumab may work synergistically with acute medications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 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) | 0.011 | 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".