Effectiveness of Yoga Therapy in the Treatment of Migraine Without Aura: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Numerous studies have explored the effectiveness of complementary and alternative medicine in the treatment of migraine but there is no documented investigation of the effectiveness of yoga therapy for migraine management. OBJECTIVES: To investigate the effectiveness of holistic approach of yoga therapy for migraine treatment compared to self-care. DESIGN: A randomized controlled trial. METHODS: Seventy-two patients with migraine without aura were randomly assigned to yoga therapy or self-care group for 3 months. Primary outcomes were headache frequency (headache diary), severity of migraine (0-10 numerical scale) and pain component (McGill pain questionnaire). Secondary outcomes were anxiety and depression (Hospital anxiety depression scale), medication score. RESULTS: After adjustment for baseline values, the subjects' complaints related to headache intensity (P < .001), frequency (P < .001), pain rating index (P < .001), affective pain rating index (P < .001), total pain rating index (P < .001), anxiety and depression scores (P < .001), symptomatic medication use (P < .001) were significantly lower in the yoga group compared to the self-care group. CONCLUSION: The study demonstrated a significant reduction in migraine headache frequency and associated clinical features, in patients treated with yoga over a period of 3 months. Further study of this therapeutic intervention appears to be 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".