Bibliographic record
Abstract
PURPOSE OF REVIEW: This article reviews the evidence base for the preventive treatment of migraine. RECENT FINDINGS: Evidence-based guidelines for the preventive treatment of migraine have recently been published by the American Academy of Neurology (AAN) and the Canadian Headache Society (CHS), providing valuable guidance for clinicians. Strong evidence exists to support the use of metoprolol, timolol, propranolol, divalproex sodium, sodium valproate, and topiramate for migraine prevention, according to the AAN. Based on best available evidence, adverse event profile, and expert consensus, topiramate, propranolol, nadolol, metoprolol, amitriptyline, gabapentin, candesartan, Petasites (butterbur), riboflavin, coenzyme Q10, and magnesium citrate received a strong recommendation for use from the CHS. SUMMARY: Migraine preventive drug treatments are underutilized in clinical practice. Principles of preventive treatment are important to improve compliance, minimize side effects, and improve patient outcomes. Choice of preventive treatment of migraine should be based on the presence of comorbid and coexistent illness, patient preference, reproductive potential and planning, and best available evidence.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".