Therapeutic effect of Chinese Tuina on diabetic peripheral neuropathy: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Clinical studies suggest that Chinese Tuina therapy may benefit diabetic peripheral neuropathy (DPN), but the evidence is inconclusive. This study evaluates its clinical efficacy and safety for DPN treatment. METHODS: Ten databases were searched, covering the period from their inception to February 21, 2024. Relevant data were extracted from studies meeting the inclusion criteria, and a meta-analysis was conducted using RevMan 5.3 software. RESULTS: A total of 24 randomized controlled trials (RCTs) involving 1,989 participants were included in the study. The meta-analysis results showed that, compared to a control group, the Chinese Tuina therapy group demonstrated a higher overall clinical efficacy rate and improved Toronto Clinical Scoring System (TCSS) scores, indicating that Chinese Tuina may provide benefits beyond conventional treatment. Furthermore, improvements were observed in the motor and sensory nerve conduction velocities (MNCV and SNCV) of certain specific nerves, such as the common peroneal nerve, sural nerve, and ulnar nerve. Although the differences in MNCV and SNCV of the tibial and median nerves were not statistically significant, the overall improvement in clinical outcome supports the conclusion that Chinese Tuina is superior to conventional treatment. CONCLUSION: Chinese Tuina therapy is a safe and effective treatment option for DPN. It can alleviate clinical symptoms and improve the MNCV of the common peroneal nerve as well as the SNCV of the sural and ulnar nerves. Its efficacy in the tibial and median nerves remains unconfirmed, highlighting a need for future large-scale, high-quality RCTs.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".