Manual Acupuncture or Combination with Vitamin B to Treat Diabetic Peripheral Neuropathy: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background and Objective. The efficacy of mecobalamin (vitamin B12) alone in the treatment of diabetic peripheral neuropathy (DPN) is often unsatisfactory, while acupuncture treatment is also controversial. This study compares manual acupuncture to vitamin B in DPN treatment. Methods. Randomized controlled trials on manual acupuncture treatment of DPN were retrieved from the Medline, Web of Science, PubMed, Cochrane Library, EMBASE, CNKI, WanFang, and VIP databases. Extracted research data were summarized in the tables, and methodological assessment was performed using the risk‐of‐bias assessment tool of Cochrane. Meta‐analysis was performed by Revman 5.3, Stata 14.0, and TSA 0.9.5.10 Beta software. Results. A total of 18 randomized clinical trials (RCTs) were recruited: (1) 11 RCTs were acupuncture alone compared with vitamin B; (2) 7 RCTs were acupuncture combined with vitamin B compared with vitamin B, involving 1200 participants. Acupuncture alone improved clinical efficacy (P < 0.05) and nerve conduction velocity of the four peripheral nerves: peroneal nerve, tibial nerve, median nerve, and ulnar nerve (P < 0.05), but there was no significant difference between the group of acupuncture alone and the group of vitamin B (P = 0.36 > 0.05) in improving median nerve SCV (sensory nerve conduction velocity). Acupuncture combined with vitamin B improved clinical efficacy and nerve conduction velocity of the three peripheral nerves, peroneal nerve, tibial nerve, and median nerve (P < 0.05), and decreased the scores of the Toronto clinical scoring system (TCSS) (P < 0.05). Conclusion. Acupuncture alone and vitamin B combined with acupuncture are more effective in treating DPN compared to vitamin B. However, more high‐quality RCTs on vitamin B combined with acupuncture are required to confirm our results.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".