Tai chi for rheumatoid arthritis: systematic review
Bibliographic record
Abstract
The objective of this systematic review is to evaluate data from controlled clinical trials testing the effectiveness of tai chi for treating rheumatoid arthritis (RA). Systematic searches were conducted on Medline, Pubmed, AMED, British Nursing Index, CINAHL, EMBASE, PsycInfo, The Cochrane Library 2007, Issue 1, the UK National Research Register and ClinicalTrials.gov, Korean medical databases, Qigong and Energy Medicine Database and Chinese databases up to January 2007. Hand-searches included conference proceedings and our own files. There were no restrictions regarding the language of publication. All controlled trials of tai chi for patients with RA were considered for inclusion. Methodological quality was assessed using the Jadad score. The searches identified 45 potentially relevant studies. Two randomized clinical trials (RCTs) and three non-randomized controlled clinical trials (CCTs) met all inclusion criteria. The included RCTs reported some positive findings for tai chi on disability index, quality of life, depression and mood for RA patients. Two RCTs assessed pain outcomes and did not demonstrate effectiveness on pain reduction compared with education plus stretching exercise and usual activity control. The extent of heterogeneity in these RCTs prevented a meaningful meta-analysis. Currently there are few trials testing the effectiveness of tai chi in the management of RA. The studies that are available are of low methodological quality. Collectively this evidence is not convincing enough to suggest that tai chi is an effective treatment for RA. The value of tai chi for this indication therefore remains unproven.
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 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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".