Efficacy and Safety of Intra-Articular Cell-Based Therapy for Osteoarthritis: Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
Objective Osteoarthritis (OA) is a chronic joint disease characterized by degeneration of articular cartilage and secondary osteogenesis. Cell-based agents, such as mesenchymal stem cells, have turned into the most extensively explored new therapeutic agents for OA. However, evidence-based research is still lacking. Methods We searched public databases up to February 2020 and only included randomized controlled trials. The outcomes included the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the Knee Injury and Osteoarthritis Outcome Score (KOOS), the visual analogue scale (VAS) score, and serious adverse events (SAEs). A network meta-analysis was also performed in this work. Results We included 13 studies in the meta-analysis. The effect size showed that cell-based therapy did not significantly reduce the WOMAC score at the 6-month follow-up (standard mean difference [SMD] −3.6; 95% confidence interval [CI] −0.90 to 0.18; P = 0.1928). However, cell-based therapy significantly improved the KOOS at the 12-month follow-up (SMD 0.68; 95% CI 0.07-1.30; P = 0.0288) and relieved pain (SMD −1.05; 95% CI −1.46 to −0.64; P < 0.0001). The findings also indicated that high-dosage adipose-derived mesenchymal stem cells (ADMSCs) may be more advantageous in terms of long-term effects. Conclusions Cell-based therapy had a better effect on KOOS improvement and pain relief without safety concerns. However, cell-based therapy did not show a benefit in terms of the WOMAC. Allogeneic cells might have advantages compared to controls in the WOMAC and KOOS scores. The long-term effect of high-dose ADMSC treatment for OA is worthy of further study.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".