Tui Na for Chronic Nonspecific Low Back Pain: Protocol for a Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Chronic nonspecific low back pain (CNLBP) is one of the most common complex pain conditions, and it is strongly associated with high rates of disability. Even though several studies on Tui na for CNLBP have been reported, to our knowledge there has been no systematic review of the currently available publications. OBJECTIVE: This study aims to develop a protocol for a systematic review and meta-analysis that will evaluate the effectiveness and safety of Tui na therapy for patients with CNLBP. METHODS: An electronic literature search of PubMed, Embase, MEDLINE, Cochrane Library, Springer, Scopus, World Health Organization International Clinical Trials Registry Platform, Physiotherapy Evidence Database (PEDro), Clarivate Analytics, and Chinese biomedical databases (the China National Knowledge Infrastructure, Wan-fang database, Chinese Scientific Journals Database, and Chinese Biomedical Literature Databases) will be conducted. Studies will be screened by two reviewers independently based on titles and abstracts, followed by a full-text reading with eligibility criteria. Randomized controlled trials involving Tui na for patients with CNLBP will be reviewed. The primary outcomes of the study are improvement of pain, analgesic medication reduction, improvement of functional disability, and degree of satisfaction with the intervention. A secondary outcome is any adverse event of Tui na intervention. Methodological quality and risk of bias will be assessed with the Cochrane Collaboration Risk of Bias Tool. If studies are sufficient, a meta-analysis of the effectiveness will be performed. If possible, we will evaluate publication bias using funnel plots. If substantial heterogeneity between studies is present, and there are sufficient studies, subgroup analyses will be conducted to explain the study findings. RESULTS: The review database searches will be initiated in December 2020, with findings expected by January 2021. CONCLUSIONS: This protocol will establish a framework of a high-quality literature synthesis on the impact of Tui na treatment in patients with CNLBP. The proposed review will determine whether Tui na is effective and safe for CNLBP patients. TRIAL REGISTRATION: PROSPERO CRD42020166731; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=166731. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/20615.
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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 | 0.080 | 0.103 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 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".