A systematic review and quality assessment of complementary and alternative medicine recommendations in insomnia clinical practice guidelines
Bibliographic record
Abstract
BACKGROUND: Sleep disorders encompass a wide range of conditions which affect the quality and quantity of sleep, with insomnia being a specific type of sleep disorder of focus in this review. Complementary and alternative medicine (CAM) is often utilized for various sleep disorders. Approximately 4.5% of individuals diagnosed with insomnia in the United States have used a CAM therapy to treat their condition. This systematic review identifies the quantity and assesses the quality of clinical practice guidelines (CPGs) which contain CAM recommendations for insomnia. METHODS: MEDLINE, EMBASE and CINAHL were systematically searched from 2009 to 2020, along with the Guidelines International Network, the National Center for Complementary and Integrative Health website, the National Institute for Health and Care Excellence, and the Emergency Care Research Institute. CPGs which focused on the treatment and/or management of insomnia in adults were assessed with the Appraisal of Guidelines, Research and Evaluation II (AGREE II) instrument. RESULTS: From 277 total results, 250 results were unique, 9 CPGs mentioned CAM for insomnia, and 6 out of the 9 made CAM recommendations relevant to insomnia. Scaled domain percentages from highest to lowest were scope and purpose, clarity of presentation, editorial independence, stakeholder involvement, rigour of development, and applicability. Quality varied within and across CPGs. CONCLUSIONS: The CPGs which contained CAM recommendations for insomnia and which scored well could be used by health care professionals and patients to discuss the use of CAM therapies for the treatment/management of insomnia, while CPGs which scored lower could be improved in future updates according to AGREE II.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".