The quantity and quality of complementary and alternative medicine clinical practice guidelines on herbal medicines, acupuncture and spinal manipulation: systematic review and assessment using AGREE II
Bibliographic record
Abstract
BACKGROUND: Complementary and alternative medicine (CAM) use is often not disclosed by patients, and can be unfamiliar to health care professionals. This may lead to underuse of beneficial CAM therapies, and overuse of other CAM therapies with little proven benefit or known contraindications. No prior research has thoroughly evaluated the credibility of knowledge-based resources. The purpose of this research was to assess the quantity and quality of CAM guidelines. METHODS: A systematic review was conducted to identify CAM guidelines. MEDLINE, EMBASE and CINAHL were searched in January 2016 from 2003 to 2015. The National Guideline Clearinghouse, National Center for Complementary and Integrative Health web site, and two CAM journals were also searched. Eligible guidelines published in English language by non-profit agencies on herbal medicine, acupuncture, or spinal manipulation for adults with any condition were assessed with the Appraisal of Guidelines, Research and Evaluation II (AGREE II) instrument. RESULTS: From 3,126 unique search results, 17 guidelines (two herbal medicine, three acupuncture, four spinal manipulation, eight mixed CAM therapies) published in 2003 or later and relevant to several clinical conditions were eligible. Scaled domain percentages from highest to lowest were clarity of presentation (85.3 %), scope and purpose (83.3 %), rigour of development (61.2 %), editorial independence (60.1 %), stakeholder involvement (52.0 %) and applicability (20.7 %). Quality varied within and across guidelines. None of the 17 guidelines were recommended by both appraisers; 14 were recommended as Yes or Yes with modifications. CONCLUSIONS: Guidelines that scored well could be used by patients and health care professionals as the basis for discussion about the use of these CAM therapies. In future updates, guidelines that achieved variable or lower scores could be improved according to specifications in the AGREE II instrument, and with insight from a large number of resources that are available to support guideline development and implementation. Future research should identify CAM therapies other than those reviewed here for which guidelines are available. Research is also needed on the safety and effectiveness of CAM therapies.
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 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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".