Diet and nutraceutical interventions for headache management: A review of the evidence
Bibliographic record
Abstract
Background The use of complementary and alternative medicines (CAM) is common among patients with primary headaches. In parallel, CAM research is growing. Diet interventions comprise another category of non-pharmacologic treatment for primary headache that is of increasing clinical and research interest. Methods A literature search was carried out to identify studies on the efficacy of diet and nutraceutical interviews for primary headache in the pediatric and adult populations. MEDLINE, Embase and EBM Reviews-Cochrane Central Register of Controlled Trials were searched to identify studies. Results There is a growing body of literature on the potential use of CAM and diet interventions for primary headache disorders. This review identified literature on the use of a variety of diet and nutraceutical interventions for headache. Most of the studies assessed the efficacy of these interventions for migraine, though some explored their role in tension-type headache and cluster headache. The quality of the evidence in this area is generally poor. Conclusions CAM is becoming more commonplace in the headache world. Several interventions show promise, but caution needs to be exercised in using these agents given limited safety and efficacy data. In addition, interest in exploring diet interventions in the treatment of primary headaches is emerging. Further research into the efficacy of nutraceutical and diet interventions is warranted.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".