Clinical efficacy and cartilage repair effect of mesenchymal stem cells intra-articular injection for knee osteoarthritis: a meta-analysis of randomized control trials
Bibliographic record
Abstract
Objective To assess the clinical efficacy and cartilage repair effect of mesenchymal stem cells (MSCs) intra-articular injection for knee osteoarthritis(KOA). Methods The databases, including Cochrane library, PubMed, Embase (via Ovid), CNKI and Wanfang, were searched from inception to March 2019. The clinical randomized controlled trials (RCTs) of MSC-based therapy in KOA were conducted. Two independent reviewers selected the studies and extracted information according to the inclusion and exclusion criteria. The quality was assessed by Cochrane Handbook's risk of bias tool. The Meta-analysis was conducted by Revman 5.3 software. I2 was used for heterogeneity test, and random effects model was used for statistical analysis. Based on the inverse-variance method, the Dersimonian & Laird method was used to introduce correction factors to correct the weights and then calculate the combined effect amount and its 95% confidence interval (CI) . Results Fifteen eligible clinical trials were included in this Meta-analysis, with a total of 576 patients. Compared with the control group, our study showed that the Visual analogue score (VAS) [MD=-15.51, 95%CI(-24.29, -6.74), P=0.000 5], Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score [MD=-11.08, 95%CI (-16.78, -5.38), P=0.000 1], and Lequesne index [MD=-8.45, 95%CI(-15.11, -1.80), P=0.01] were significantly decreased in the MSCs treatment group. However, there was no statistical significance in Lysholm knee score and MRI evaluation results (P>0.05). Conclusion This Meta-analysis has demonstrated that intra-articular injection of MSCs has good efficacy and safety in pain control and functional improvement in patients with KOA. But the results of the study do not show evidence of MSCs in repairing damaged cartilage. Key words: Osteoarthritis; Stem cells; Cartilage; Meta-analysis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.047 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.062 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".