P.004 Real-world effectiveness of intravenous eptinezumab in patients with chronic migraine and previous subcutaneous preventive migraine treatment
Bibliographic record
Abstract
Background: Since 2018, several CGRP-targeted therapies have entered the migraine market, including eptinezumab. Minimal evidence exists evaluating the real-world effectiveness of switching from a subcutaneous to an intravenous anti-CGRP mAb. Methods: An observational, multi-site (n=4), US-based study, REVIEW evaluated real-world experiences of patients with chronic migraine (CM) treated with eptinezumab using a chart review, patient survey, and physician interviews. Adults (≥18 years) with a diagnosis of CM who had completed ≥2 consecutive eptinezumab infusion cycles were eligible. Results: Enrolled patients were primarily female (83%, 78/94), had a mean age of 49 years and a mean migraine diagnosis duration of 15.4 years. All patients (94/94) self-reported prior preventive therapy with 89% (84/94) reporting prior subcutaneous anti-CGRP mAb use (i.e., fremanezumab, galcanezumab, or erenumab). Regardless of prior exposure to a CGRP ligand or receptor blocker, the number of “good” days/month more than doubled following eptinezumab. Patients experienced a similar mean change in the number of “good” days/month regardless of the number and type of previous subcutaneous anti-CGRP mAb used. Conclusions: This real-world, patient survey showed that patients with prior exposure to subcutaneous anti-CGRP mAbs had high overall satisfaction with the effectiveness of eptinezumab treatment regardless of the number and type of previous therapies used.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".