The State of the Evidence for Whole-System, Multi-Modality Naturopathic Medicine: A Systematic Scoping Review
Bibliographic record
Abstract
OBJECTIVE: To summarize the current state of the research evidence for whole-system, multi-modality naturopathic medicine. DESIGN: A systematic search for research articles from around the world was undertaken using MEDLINE, Embase, CINAHL, AMED, and WHO regional indexes. Naturopathic journals and gray literature were hand searched. No language restrictions were imposed. INTERVENTIONS: All human research evaluating the effectiveness of naturopathic medicine, where two or more naturopathic modalities are delivered by naturopathic clinicians, were included in the review. Case studies of five or more cases were included. RESULTS: Thirty-three published studies (n = 9859) met inclusion criteria (11 American; 4 Canadian; 6 German; 7 Indian; 3 Australian; 1 United Kingdom; and 1 Japanese) across a range of mainly chronic clinical conditions. The studies predominantly showed evidence for the efficacy of naturopathic medicine for the conditions and settings in which they were based. CONCLUSIONS: To date, research in whole-system, multi-modality naturopathic medicine shows that it is effective for treating cardiovascular disease, musculoskeletal pain, type 2 diabetes, polycystic ovary syndrome, depression, anxiety, and a range of complex chronic conditions.
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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 | 0.026 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".