Migraine and Its Treatment with 5‐HT<sub>1B/1D</sub> Agonists (Triptans)
Bibliographic record
Abstract
PURPOSE: To review the epidemiology and clinical features of migraine and to discuss the use of the 5-HT1B/1D agonists (triptans) in the treatment of moderate to severe migraine. DATA SOURCES: A Medline search was conducted for relevant recent articles on migraine and the efficacy and safety of the triptans. CONCLUSIONS: With the advent of a standardized classification system for headache to simplify migraine diagnosis, new approaches to treatment, and effective new therapies, such as the triptans, many patients have obtained significant relief from the pain and disability associated with migraine. IMPLICATIONS FOR PRACTICE: The key to successful migraine management is to provide the most effective treatment at the earliest possible time. Under the step-care approach to migraine management, the mildest and most conservative treatment was recommended as a first step, without regard for the degree of the patient's pain or disability. This approach has been replaced by stratified care, in which migraine management is based on the severity of the patient's pain and disability. Under the stratified approach, patients with moderate or severe migraine would be prescribed effective migraine-specific drugs, such as the triptans, as first-line therapy.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.004 | 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".