Complementary and Alternative Medicine Use in Juvenile Idiopathic Arthritis: A Systematic Review of Prevalence and Evidence
Bibliographic record
Abstract
Juvenile idiopathic arthritis (JIA) is a chronic condition that affects children. Healthcare for JIA is aimed at symptom management and as a result, complementary and alternative medicine (CAM) is readily sought. The objective of this manuscript is to provide healthcare professionals and researchers with a comprehensive review of the prevalence of CAM use in JIA, determinants of use, and outcomes associated with various therapies. The implications for future clinical practice and promising areas of investigation will be discussed. An in-depth search was conducted in MEDLINE, EMBASE, AMED, and Cochrane Library. Programs from relevant conferences were also searched. Thirty-eight articles were retrieved and 12 were included in the analysis. Eight articles assessed the prevalence of CAM use in JIA, three investigated specific interventions (Tripterygium wilfordii Hook F, relaxation, massage), and one examined reasons for using CAM. Results showed that CAM use is relatively high among JIA patients, but prevalence rates vary due to differences in methodology and definitions of CAM. Most patients use CAM for pain relief. Results from intervention studies are preliminary and should be followed with methodologically rigorous studies. Healthcare professionals should become familiar with CAM so as to provide optimal support and care for children with JIA.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".