Identification of predictors of response to Erenumab in a cohort of patients with migraine
Bibliographic record
Abstract
Background: The migraine-specific monoclonal antibody Erenumab targeting the calcitonin gene related peptide receptor is an effective and well tolerated preventive treatment of episodic and chronic migraine. However, its price limits its use as a first line therapy against migraine. Therefore, identifying patients who will adequately respond to such treatment is paramount. Methods: In this retrospective, real-life cohort study, 172 adult patients with refractory episodic or chronic migraine treated with Erenumab were included. To identify the predictors of response to Erenumab, bivariate subgroup analysis of several potential factors was performed, and multivariate logistic regression modeling was done to obtain Odds Ratio (OR). Results: Of the 172 patients, 57.0% achieved a successful treatment response (reduction of monthly migraine days by ≥50%). Statistically significant predictors of a treatment response were the presence of chronic migraine, tension-type headache, and a positive response to triptan with an odd ratio of 0.473 (95% CI, 0.235–0.952), 0.485 (95% CI, 0.245–0.962) and 3.985 (95% CI, 1.811–8.770), respectively (P < 0.05). Conclusions: Successful Erenumab treatment response rate was 57.0% in this retrospective cohort. As chronic migraine and tension-type headache were negative predictors of Erenumab response while triptan response was a positive predictor, this data suggests the potential for Erenumab monotherapy without the need for traditional preventive treatment in refractory migraine sufferers improving side effect profile and treatment adherence for a cohort of patients difficult to treat.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".