P.015 Monthly migraine days, acute medication use-days, and migraine-specific quality of life in responders to atogepant: a post hoc analysis
Bibliographic record
Abstract
Background: In phase 3 ADVANCE, atogepant 60mg reduced mean monthly migraine days (MMDs) from 7.8 days (baseline) to 3.0 (weeks 9-12; Δ=−4.7) in the overall episodic migraine population [treatment responders and nonresponders (i.e., marked benefit and minimal benefit)], which obscures information regarding magnitude of treatment effect in these populations. Here, magnitude of treatment effect in atogepant responders and nonresponders is characterized. Methods: Mean MMDs, acute medication use-days (MUDs), and Migraine-Specific Quality of Life-Role Function-Restrictive (MSQ-RFR) scores were calculated in treatment responders (based on MMD percentage reduction) and nonresponders from ADVANCE participants. Results: From baseline to weeks 9-12, ≥50% improvement was achieved by 71% (139/195) of participants. In these responders, MMDs reduced from 7.6 to 1.3 (Δ=−6.3). 50% (97/195) of participants achieved ≥75% response. In this group, MMDs reduced from 7.7 to 0.6 (Δ=−7.1). Atogepant 60mg nonresponders (<25% reduction in MMDs; 15% [30/195 participants]) showed MMD change from 7.7 to 9.1 (Δ=+1.4). Acute MUDs in ≥50% MMD responders decreased 7.1 to 1.6 (Δ=−5.5). In treatment-nonresponders, acute MUDs were 7.3 (baseline) and 7.2 (weeks 9-12; Δ=−0.1). Similar mean MSQ-RFR score changes were observed in both populations. Conclusions: Of participants who experienced ≥50% reduction in MMDs, 71% had substantial treatment effect (ΔMMD=−6.3), representing 83% reduction in MMDs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".