A scoping review of network meta-analyses assessing the efficacy and safety of complementary and alternative medicine interventions
Bibliographic record
Abstract
BACKGROUND: Network meta-analysis (NMA) has rapidly grown in use during the past decade for the comparison of healthcare interventions. While its general use in the comparison of conventional medicines has been studied previously, to our awareness, its use to assess complementary and alternative medicines (CAM) has not been studied. A scoping review of the literature was performed to identify systematic reviews incorporating NMAs involving one or more CAM interventions. METHODS: An information specialist executed a multi-database search (e.g., MEDLINE, Embase, Cochrane), and two reviewers performed study selection and data collection. Information on publication characteristics, diseases studied, interventions compared, reporting transparency, outcomes assessed, and other parameters were extracted from each review. RESULTS: A total of 89 SR/NMAs were included. The largest number of NMAs was conducted in China (39.3%), followed by the United Kingdom (12.4%) and the United States (9.0%). Reviews were published between 2010 and 2018, with the majority published between 2015 and 2018. More than 90 different CAM therapies appeared at least once, and the median number per NMA was 2 (IQR 1-4); 20.2% of reviews consisted of only CAM therapies. Dietary supplements (51.1%) and vitamins and minerals (42.2%) were the most commonly studied therapies, followed by electrical stimulation (31.1%), herbal medicines (24.4%), and acupuncture and related treatments (22.2%). A diverse set of conditions was identified, the most common being various forms of cancer (11.1%), osteoarthritis of the hip/knee (7.8%), and depression (5.9%). Most reviews adequately addressed a majority of the PRISMA NMA extension items; however, there were limitations in indication of an existing review protocol, exploration of network geometry, and exploration of risk of bias across studies, such as publication bias. CONCLUSION: The use of NMA to assess the effectiveness of CAM interventions is growing rapidly. Efforts to identify priority topics for future CAM-related NMAs and to enhance methods for CAM comparisons with conventional medicine are needed. SYSTEMATIC REVIEW REGISTRATION: https://ruor.uottawa.ca/handle/10393/35658.
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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 | 0.090 | 0.293 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".