Headache and migraine clinical practice guidelines: a systematic review and assessment of complementary and alternative medicine recommendations
Bibliographic record
Abstract
BACKGROUND: Globally, 3 billion people suffer from either migraine or tension-type headache disorder over their lifetime. Approximately 50% of American adults suffering from headache or migraine have used complementary and alternative medicine (CAM), however, the quality and quantity of recommendations associated with such therapies across clinical practice guidelines (CPGs) for the treatment and/or management of these conditions are unknown. The purpose of this study was to identify the quantity and assess the quality of such CAM recommendations. METHODS: MEDLINE, EMBASE and CINAHL were systematically searched from 2009 to April 2020; the Guidelines International Network and the National Center for Complementary and Integrative Health websites were also searched for eligible CPGs. CPGs were included if they provided any therapy recommendations. Eligible CPGs included those written for adult patients with headache and migraine; CPGs containing CAM recommendations were assessed twice for quality using the AGREE II instrument, once for the overall CPG and once for the CAM sections. RESULTS: Of 486 unique search results, 21 CPGs were eligible and quality assessed; fifteen CPGs mentioned CAM, of which 13 CPGs made CAM recommendations. The overall CPG assessment yielded higher scaled domain percentages than the CAM section across all domains. The results from highest to lowest were as follows (overall, CAM): clarity of presentation (66.7% vs. 50.0%), scope and purpose (63.9% vs. 61.1%), stakeholder involvement (22.2% vs. 13.9%), rigour of development (13.5% vs. 9.4%), applicability (6.3% vs. 0.0%), and editorial independence (0.0% vs. 0.0%). CONCLUSIONS: Of the eligible CPGs, the CAM sections were of lower quality compared to the overall recommendations across all domains of the AGREE II instrument. CPGs that scored well could serve as a framework for discussion between patients and healthcare professionals regarding use of CAM therapies in the context of headache and migraine.
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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 | 0.041 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".