Mesenchymal stem cells therapy for the treatment of non-union fractures: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This meta-analysis aimed to pool the existing evidence to determine the clinical efficacy and safety of mesenchymal stem cells (MSC) in patients with non-unions. METHODS: A systematic search in PubMed and Scopus was performed until October 2024 to gather pertinent studies. The inclusion criteria included participants with non-unions, the intervention of MSC administration, a comparator of standard treatment (bone graft), and outcomes focused on healing rate, healing time, or side effects. The Jadad score Newcastle-Ottawa Scale (NOS) was used to assess the risk of bias in randomized and non-randomized studies, respectively. Moreover, GRADE criteria were used to assess the quality of evidence. Using a random effects model, odds ratios (OR) with 95% confidence intervals (CIs) were calculated for healing and complication rates, while standardized mean differences (SMD) with their 95% CIs were used to assess the impact of MSC therapy on bone union time. RESULTS: Twenty-one studies, with 866 patients, were included. The bone healing rates were 44% at 3 months, 73% at 6 months, 90% at 9 months, and 86% at 12 months, eventually reaching 91% after 12 months of follow-up. MSC therapy, with or without scaffolds, was linked to higher odds of bone healing rate at 3 and 6 months, compared to bone grafts as the standard care (OR = 1.69). The time to union following the treatment was 6.30 months (95%CI: 86-96%), with patients treated with MSC/Scaffold experiencing a shorter time compared to MSC alone (5.85 vs. 6.36 months). MSC therapy significantly decreased bone union time (SMD:-0.54 months, 95% CI: -0.75 to -0.33). The complication rate was 1% (MSC/Scaffold: 0%, MSC alone: 2%), with MSC alone or MSC/Scaffold showing a lower risk than the standard care (OR = 0.41, 95% CI: 0.22-0.78). CONCLUSION: MSC is a potential adjunct therapy for patients with non-union fractures. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".