Galcanezumab: a humanized monoclonal antibody for the prevention ofmigraine and cluster headache
Bibliographic record
Abstract
Migraine is a common, painful and highly disabling neurological condition that has plagued mankind for millennia, but its pathophysiology remained largely obscure until recently. The clinical success of triptans for treating migraine and the discovery that calcitonin gene-related peptide (CGRP) plays a prominent role in migraine led to increased research interest into this disease. An important improvement has been the development of monoclonal antibodies, including galcanezumab, that bind to CGRP or to its receptor, preventing its activation. Subsequent clinical trials have reported that galcanezumab is safe and well tolerated, and is effective in reducing the frequency of migraine attacks in patients with episodic or chronic migraine. At the same time, increased study of the pathophysiology of cluster headache, a relatively rare condition with excruciatingly painful headache attacks (i.e., "suicide headaches"), led to the discovery that, as in migraine, CGRP plays an important role in its pathology. Clinical trials suggest that galcanezumab is safe and effective for the prevention of episodic cluster headache, and it is under study for chronic cluster headache. Galcanezumab is approved for the prevention of migraine in the U.S., the European Union, Canada and Mexico, and was also approved for the treatment of episodic cluster headache in the U.S.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".