Baduanjin exercise for patients with knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is a common chronic degenerative disorder with an increasingly prevalence among the older individuals and the leading cause of pain in the elderly. Baduanjin, one of the ancient traditional Chinese mind-body exercise routine, has been recognized to have clinical benefits for KOA. We aim to evaluate the efficacy and safety of Baduanjin for patients with KOA through this systematic review and meta-analysis. METHODS: Four English databases (Cochrane Central Register of Controlled Trials, PubMed, EMBASE, and Web of Science), and 4 Chinese databases (China National Knowledge Infrastructure, Chinese Biomedical Literature Database, Chinese Scientific Journal Database, and Wanfang Database), will be searched from establishment of the database until October 2020. The reference lists and the citation lists of studies meeting the inclusion criteria will also be searched to identify further studies for inclusion. The search languages are English and Chinese. The randomized controlled trials of Baduanjin training for patients with KOA will be included. The primary outcome will be assessed according to the Western Ontario and McMaster Universities Osteoarthritis Index. Meta-analysis will be conducted with the use of RevMan 5.3. RESULTS: The results of this research will be submitted to a peer-reviewed publications. CONCLUSION: This systematic review aims to present evidence for whether Baduanjin training is an effective intervention which can improve both physical condition and life quality in patients suffering KOA. INPLASY REGISTRATION NUMBER: INPLASY202090051.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".