Efficacy and safety of mesenchymal stem cell injections for knee osteoarthritis: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: There have not been any clear studies on the use of mesenchymal stem cells (MSCs) to treat osteoarthritis (OA) in the knee. Materials and Methods: This study investigates the effects of different MSC dosages on pain alleviation in individuals with OA in the knee by conducting a meta-analysis of existing randomized controlled trials. Electronic resources such as Google Scholar, PubMed, Cochrane Library, and Web of Science were searched up until June 2023. Treatment effect sizes were computed using the knee osteoarthritis outcome score (KOOS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Knee Society Score (KSS). Random or fixed effect models were applied to aggregate the data. We performed a subgroup analysis according to dosage level. The heterogeneity of the research was investigated using the Chi-square test and the I2 index. Results: The meta-analysis included 26 studies with a total sample size of 739 patients. A significant reduction in pain was observed 1 year and 2 years following the injection of MSCs into the injured joint, as indicated by the Visual Analogue Scale, WOMAC, KOOS, and KSS indexes (P < 0.05). Patients on MSCs reported much reduced pain after 1 and 2 years compared to the control group (P < 0.05). Subgroup and meta-regression analyses revealed no statistically significant variations in the effectiveness of MSC dosage (P < 0.05). The studies did not report any adverse effects. Conclusion: Different dosages of MSCs had the same pain-relieving effects on patients with OA in the knee. MSC injections were safe and beneficial in such cases.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.047 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".