Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it