Efficacy of Traditional Chinese Exercise in the Treatment of Knee Osteoarthritis: A protocol for a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Review question / Objective: To evaluate the efficacy of Traditional Chinese Exercises (TCEs) in the treatment of knee osteoarthritis.Information sources: The following databases will be searched comprehensively from the construction to April 1, 2022.It includes three English databases, that is, PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE.At the same time,one Chinese databases should be involved, which is China National Knowledge Infrastructure (CNKI).Main outcome(s): The primary outcome measure was the content of pain on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale, with stiffness and physical function as secondary outcome measures.INPLASY registration number: This protocol was registered with the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) on 26 April 2022 and was last u p d a t e d o n 2 6 A p r i l 2 0 2 2 ( r e g i s t r a t i o n n u m b e r INPLASY202240154).
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 | 0.052 | 0.056 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.022 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".