Bibliographic record
Abstract
BACKGROUND: Feverfew (Tanacetum parthenium L.) is a popular herbal remedy for migraine. OBJECTIVES: To systematically review the evidence for or against the efficacy of feverfew versus placebo for the prevention of migraine. SEARCH STRATEGY: Electronic literature searches were performed using the databases CISCOM (Research Council for Complementary Medicine, London, UK), MEDLINE, EMBASE, Biosis and the Cochrane Library (each from its inception to April 1998). Manufacturers were contacted and the bibliographies of identified articles checked for further trials. SELECTION CRITERIA: Randomised, placebo-controlled, double-blind trials assessing the efficacy of feverfew for preventing migraine were included. No restrictions regarding the language of publication were imposed. DATA COLLECTION AND ANALYSIS: Data on patients, interventions, methods, outcomes and results were extracted in a pre-defined, standardised manner. Methodological quality was evaluated using the scoring system developed by Jadad and colleagues. Both data extraction and the assessment of methodological quality were performed independently by two reviewers. MAIN RESULTS: Four trials met the inclusion criteria. The majority of these trials suggested beneficial effects of feverfew compared with placebo. However, the trial with the highest methodological quality, which was also among the largest, found no significant difference between feverfew and placebo. REVIEWER'S CONCLUSIONS: The efficacy of feverfew for the prevention of migraine has not been established beyond reasonable doubt.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".