Pharmacological Acute Migraine Treatment Strategies: Choosing the Right Drug for a Specific Patient
Bibliographic record
Abstract
ABSTRACT: Background: In our targeted review (Section 2), 12 acute medications received a strong recommendation for use in acute migraine therapy while four received a weak recommendation for use. Strong recommendations were made to avoid use of two other medications, except for exceptional circumstances. Two anti-emetics received strong recommendations for use as needed. Objective: To organize the available acute migraine medications into acute migraine treatment strategies in order to assist the practitioner in choosing a specific medication(s) for an individual patient. Methods: Acute migraine treatment strategies were developed based on the targeted literature review used for the development of this guideline (Section 2), and a general literature review. Expert consensus groups were used to refine and validate these strategies. Results: Based on evidence for drug efficacy, drug side effects, migraine severity, and coexistent medical disorders, our analysis resulted in the formulation of eight general acute migraine treatment strategies. These could be grouped into four categories: 1) two mild-moderate attack strategies, 2) two moderate-severe attack or NSAID failure strategies, 3) three refractory migraine strategies, and 4) a vasoconstrictor unresponsive-contraindicated strategy. In addition, strategies were developed for menstrual migraine, migraine during pregnancy, and migraine during lactation. The eight general treatment strategies were coordinated with a “combined acute medication approach” to therapy which used features of both the “stratified” and the “step care across attacks” approaches to acute migraine management. Conclusions: The available medications for acute migraine treatment can be organized into a series of strategies based on patient clinical features. These strategies may help practitioners make appropriate acute medication choices for 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".