A.2 Identification of predictors of response to Erenumab in episodic and chronic migraine in a cohort of patients: a preliminary analysis
Bibliographic record
Abstract
Background: Erenumab is an antibody anti-calcitonin gene related peptide (CGRP) receptor approved for the treatment of episodic (EM) and chronic migraine (CM). In this study, we aimed to identify the predictors of response to the treatment. Methods: This is an ongoing retrospective cohort study of 120 patients (49 with cervicalgia) with EM or CM treated with Erenumab. The first endpoint was to identify the success rate of this treatment (at least 50% reduction in monthly migraine days during the third month of the treatment). The second endpoint was to identify the predictors of response to Erenumab treatment. Results: Seventy one percent of patients achieved a favorable response (P-value<0.001) to Erenumab. Patients with cervicalgia showed a lower treatment success rate (21.1% with vs 40.8% without cervicalgia) while patients without cervicalgia showed a higher treatment success rate (78. 9% without vs 59.2% with cervicalgia) with a P-value of 0.025 and an odd ratio of 0.388 (95% CI 0.174-0.869, P-value=0.021). A similar trend was observed in patients with occipital neuralgia and obesity (P-value<0.08). Conclusions: The preliminary analysis of this study demonstrates that cervicalgia (and to a lesser extend occipital neuralgia and obesity) is a negative predictor of response to Erenumab in patients with migraine.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".