Five Traditional Chinese Medicine External Treatment Methods Combined with Mecobalamin for Diabetic Peripheral Neuropathy: A Network Meta-Analysis
Bibliographic record
Abstract
Background: Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications of diabetes. Traditional Chinese medicine (TCM) external treatment has been widely used in China as adjunctive treatment, and some small sample clinical studies have proved its effectiveness. However, due to the limited number of studies, we used network meta-analysis to compare the effectiveness of 5 commonly used external treatment methods of traditional Chinese medicine in the treatment of diabetic peripheral neuropathy. Methods: We searched PubMed, EMBASE, The Cochrane Library, Web of Science, CNKI, CBM, WanFang Knowledge Service Platform, and VIP databases and collected and screened randomised controlled trials on the external treatment of traditional Chinese medicine combined with mecobalamin in the treatment of DPN according to the inclusion and exclusion criteria. The search period was from 2011 to May 2021. The quality of included studies was assessed using the revised Cochrane risk-of-bias tool for randomized trials. The outcome indicators are Toronto score, median nerve sensory conduction velocity, and median nerve motor conduction velocity. Results: A total of 22 publications were included in the study. The results of the network meta-analysis showed that acupuncture combined with mecobalamin was superior to other TCM external treatments combined with mecobalamin in terms of decreasing the Toronto score (MD = -2.8, 95% CI: -5.2∼-0.49), improving median nerve sensory conduction velocity (MD = 3.6, 95% CI: 2.4∼4.9), and median nerve motor conduction velocity (MD = 4.5, 95% CI: 2.6∼6.5). The SUCRA value and probability ranking chart showed that among the three outcome indicators, acupuncture combined with mecobalamin was the best, followed by acupoint injection combined with mecobalamin. Conclusion: In this network meta-analysis, acupuncture combined with mecobalamin shows the best results in the treatment of DPN, followed by acupoint injection combined with mecobalamin.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.064 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".