P.019 Interictal burden of migraine: correlations with other measures of migraine burden and effects of galcanezumab migraine-preventive treatment
Bibliographic record
Abstract
Background: Typical migraine clinical trial endpoints assess only ictal burden. Methods: Adults (N=462) with episodic or chronic migraine with previous failure of 2-4 preventive medication categories were randomized 1:1 to 3-month double-blind treatment with placebo or galcanezumab 120mg. Primary endpoint was mean change from baseline in monthly migraine headache days. Migraine Interictal Burden Scale-4 (MIBS-4) measured migraine-related burden on non-headache days for past 4 weeks (0=no burden, 1-2=mild, 3-4=moderate, 5-12=severe). Migraine Disability Assessment (MIDAS), Migraine-Specific Quality of Life Questionnaire (MSQ), Patient Global Impression-Severity (PGI-S), depression (Patient Health Questionaire-9 [PHQ-9]), and anxiety (Generalized Anxiety Disorder Scale [GAD-7]) were assessed. Relationships among measures were assessed at baseline using Spearman’s rank correlation coefficient. Results: MIBS-4 was moderately correlated with PHQ-9 (r=.55) and MSQ total (r=−.53). Other correlations with MIBS-4 were low (GAD-7, r=0.42; MIDAS, r=0.41; PGI-S, r=0.32) or negligible (migraine headache days, r=0.22). After 3 months, from a mean baseline of 13.2 monthly migraine headache days, galcanezumab patients improved by 4.4 vs 1.3 days for placebo (p<.0001). From mean MIBS-4 baseline of 5.5, galcanezumab patients improved by 1.8 vs 0.8 points for placebo (p<.0001). Conclusions: Galcanezumab significantly reduced ictal and interictal burden of migraine. Results suggest interictal burden is a distinct effect of the disease.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".