MétaCan
Menu
Back to cohort
Record W4380049076 · doi:10.3389/fpubh.2023.1168167

Efficacy of traditional Chinese exercise for the treatment of pain and disability on knee osteoarthritis patients: a systematic review and meta-analysis of randomized controlled trials

2023· review· en· W4380049076 on OpenAlexaboutno aff
Shuaipan Zhang, Ruixin Huang, Guangxin Guo, Lingjun Kong, Jianhua Li, Qingguang Zhu, Min Fang

Bibliographic record

VenueFrontiers in Public Health · 2023
Typereview
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersShanghai University of Traditional Chinese MedicineScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of China
KeywordsMedicineWOMACOsteoarthritisRandomized controlled trialPhysical therapyConfidence intervalStrictly standardized mean differenceMeta-analysisPopulationSubgroup analysisInternal medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

Objective To evaluate the efficacy of Traditional Chinese Exercises (TCEs) in treating knee osteoarthritis (KOA). Methods Four databases without language or publication status restrictions were searched until April 1, 2022. Based on the principle of Population, Intervention, Comparison, Outcomes and Study design, the researchers searched for randomized controlled trials of TCEs in treating KOA. The Western Ontario and McMaster Universities Osteoarthritis (WOMAC) pain was defined as the primary outcome, whereas stiffness and physical function were the secondary outcomes. Subsequently, two researchers conducted the process independently, and the data were analyzed using the RevManV.5.3 software. Results Overall, 17 randomized trials involving 1174 participants met the inclusion criteria. The synthesized data of TCEs showed a significant improvement in WOMAC pain score [standardized mean difference (SMD) = −0.31; 95% confidence interval (CI): −0.52 to −0.10; p = 0.004], stiffness score (SMD = −0.63; 95% CI: −1.01 to −0.25; p = 0.001) and physical function score (SMD = −0.38; 95% CI: −0.61 to −0.15; p = 0.001) compared with the control group. Sensitivity analyses were performed to determine the combined results' stability, which was unstable after excluding articles with greater heterogeneity. A further subgroup analysis showed that it might be the reason for the heterogeneity of the different traditional exercise intervention methods. Additionally, it showed that the Taijiquan group improved pain (SMD = 0.74; 95% CI: −1.09 to 0.38; p < 0.0001; I2 = 50%), stiffness (SMD = −0.67; 95% CI −1.14 to 0.20; p = 0.005) and physical function score (SMD = −0.35; 95% CI: −0.54 to 0.16; p = 0.0003; I2 = 0%) better than the control group. The Baduanjin group improved stiffness (SMD = −1.30; 95% CI: −2.32 to 0.28; p = 0.01) and physical function (SMD = −0.52; 95% CI: −0.97 to 0.07; p = 0.02) better than the control group. However, the other interventions showed no difference compared with the control group. Conclusion This systematic review provides partial evidence of the benefits of TCEs for knee pain and dysfunction. However, due to the heterogeneity of exercise, more high-quality clinical studies should be conducted to verify the efficacy. Systematic review registration https://inplasy.com/inplasy-2022-4-0154/ , identifier: International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY) [INPLSY202240154].

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 imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.037
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0260.031
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.125
GPT teacher head0.374
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Public HealthSame topicOsteoarthritis Treatment and MechanismsFrench-language works237,207