Human studies of the efficacy and safety of stem cells in the treatment of diabetic peripheral neuropathy: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the efficacy and safety of stem cell therapy in human studies for diabetic peripheral neuropathy (DPN). METHODS: A comprehensive literature review was performed across multiple databases, including Ovid MEDLINE ALL, Embase via Ovid SP, Scopus, Web of Science Core Collection, and Cochrane CENTRAL, up to January 31, 2024. Keywords and controlled vocabularies related to diabetic neuropathy and stem cell therapy were used. Inclusion criteria encompassed all controlled trials examining stem cell therapy for DPN, excluding animal or in vitro studies, review papers, conference abstracts, and editor letters. Data extraction and risk of bias assessment were independently performed by multiple reviewers using standardized tools. RESULTS: Out of 5431 initial entries, seven were included. Stem cell therapies included bone marrow-derived mononuclear cells and umbilical cord-derived mesenchymal stem cells, administered mainly via intramuscular transplantation. Meta-analysis indicated significant improvements in motor nerve conduction velocity (weighted mean differences (WMD): 2.2, 95% CI 1.6-2.8) and sensory nerve conduction velocity (WMD: 1.9, 95% CI 1.1-2.6). Vibration perception threshold and Toronto Clinical Scoring System scores decreased significantly (WMD: - 2.9, 95% CI - 4.0, - 1.8, and WMD: - 3.6, 95% CI - 5.0, - 2.2, respectively). Sensitivity analysis and subgroup analysis confirmed the robustness and specificity of these findings. The complications were pain and swelling at the injection sites, which disappeared in a few days. CONCLUSION: Stem cell therapy shows significant promise in improving clinical outcomes for DPN, with evident benefits in nerve conduction and sensory parameters. Further research is needed to consolidate these findings and optimize therapeutic protocols.
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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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".