Mesenchymal Stem Cells Injection Is More Effective Than Hyaluronic Acid Injection in the Treatment of Knee Osteoarthritis With Similar Safety: A Systematic Review and Meta‐analysis
Bibliographic record
Abstract
PURPOSE: To evaluate the efficacy and safety of intra-articular injection of mesenchymal stem cells (MSCs) versus hyaluronic acid (HA) in the treatment of knee osteoarthritis (KOA). METHODS: Eligible randomized controlled trials (RCTs) were identified through a search of PubMed, Embase, the Cochrane Library, Web of Science, SinoMed, and CNKI databases from inception to March 2024. For meta-analysis, data on clinical outcomes were measured using visual analog scale (VAS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and data on cartilage repair were measured using the Whole-Organ Magnetic Resonance Imaging Score (WORMS); data on safety were evaluated by the incidence of adverse events. Two researchers independently read the included literature, extracted data and evaluated the quality, used the Cochrane risk bias assessment tool for bias risk assessment, and used RevMan5.3 software for meta-analysis. RESULTS: = 0) between the 2 groups (P > .05). CONCLUSIONS: In terms of efficacy, the clinical effects of intra-articular injection therapy using MSCs for KOA are superior to those of HA, and the cartilage repair effect of MSCs is also markedly better than that of HA. Although the clinical effects varied across time periods, the functional score reached the minimum clinically significant difference at both 6 and 12 months. In terms of safety, adverse reactions mainly manifest as joint pain, swelling, and joint effusion. Both intra-articular injections of MSCs and HA did not result in severe adverse reactions, indicating that MSCs and HA have similar safety profiles. LEVEL OF EVIDENCE: Level I, meta-analysis of Level I studies.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.044 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".