Calcitonin gene-related peptide–targeted therapies for migraine
Bibliographic record
Abstract
Calcitonin gene-related peptide (CGRP)-targeted therapies are the first medications developed specifically for migraine prevention. They block the actions of CGRP, a neuropeptide with a key role in migraine pathophysiology. There are 2 categories of drugs: monoclonal antibodies directed against either the CGRP ligand or receptor, and small-molecule CGRP receptor antagonists. CGRP monoclonal antibodies are available as self-administered subcutaneous injections or as an intravenous infusion, and are administered monthly or quarterly. Clinical trial and real-world data over the past 10 years support their effectiveness and safety in patients with episodic and chronic migraines, and research into long-term safety is ongoing. Patients must fulfil certain criteria, including prior treatment with nonspecific oral preventive medications, to receive subsidised treatment with these drugs on the Pharmaceutical Benefits Scheme (PBS) in Australia. Small-molecule CGRP receptor antagonists (known as gepants) are orally administered drugs that can be used for migraine prevention or acute treatment. There are no gepants listed on the PBS at the time of writing. Their role in the prevention and acute treatment of migraine is continuing to evolve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.006 | 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".