Predictors of clinical response to erenumab in patients with migraine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Anti-CGRP monoclonal antibodies have emerged as efficacious preventive therapies for some, but not all patients with migraine. It is not yet fully understood what predicts treatment response. Objective: To identify factors associated with good or poor response to erenumab, the first available CGRP monoclonal antibody. Methods: A chart review of patients with migraine from a large headache center who received at least three 4-weekly doses of erenumab between 2018 and 2020 was conducted. Clinical variables were compared between erenumab responders (defined as ≥30% reduction in monthly headache or migraine days at 3 months) and non-responders via logistic regression analyses. Results: Among 90 enrolled patients, 62.2% were erenumab responders and 37.8% non-responders. A significantly larger proportion of non-responders were unemployed (58.8% vs. 28.6%), had complex diagnosis (chronic migraine overlapping another primary or secondary headache) (47.1% vs. 14.3%), higher monthly headache days (30 vs. 25.5) and migraine days (20 vs. 12), a higher frequency of daily headache (76.5% vs. 48.2%), and failed more preventive therapies (5.5 vs. 3). Based on logistic regressions, erenumab responsiveness did not significantly associate with duration of migraine, presence of aura, medication overuse, number of concurrent preventives, response to onabotulinumtoxinA or triptans, or certain comorbidities and substance use. Conclusions: This work may help improve selection of patients who may benefit from erenumab, but further prospective research studies are needed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it