Entering the era of living biopharmaceuticals for treating knee osteoarthritis: A systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND Knee osteoarthritis (KOA) is a leading cause of arthritis-related morbidity. Mesenchymal stem cells (MSCs), as living biopharmaceuticals, have emerged as a potential treatment option due to their anti-inflammatory and immunomodulatory properties. AIM To compare the safety and efficacy of allogenic MSCs (AlloMSCs) vs autologous MSCs (AutoMSCs) in treating KOA in clinical settings. METHODS We conducted a systematic review and network meta-analysis to compare the safety and efficacy of AlloMSCs vs AutoMSCs in treating KOA. Our systematic search of four databases, including PubMed, Cochrane, Embase, and ClinicalTrials.gov, identified relevant randomized controlled trials (RCTs) reporting MSC-based treatment for KOA and reporting visual analog scale, Western Ontario and McMaster Universities Osteoarthritis scores, and adverse events. We assessed the methodological quality of the studies using the Cochrane Collaboration tool and calculated risk ratios (RRs) and weighted mean differences [with 95% confidence intervals (CIs)]. Our statistical analyses used the R-Studio network meta-packages (version 2023.12.0). The study protocol was pre-registered on the International Prospective Register of Systematic Reviews (ID: CRD42024590866). RESULTS Nineteen RCTs involving 1216 patients with KOA met the inclusion criteria of the study. The network meta-analysis showed that AlloMSCs gave a significant reduction in visual analog scale scores by 14.91 points (95%CI: -24.52 to -5.30) vs 12.95 points with AutoMSCs (95%CI: -24.42 to -1.48). For Western Ontario and McMaster Universities Osteoarthritis score, AlloMSCs led to a significant reduction of 23.12 points (95%CI: -31.15 to -15.10) compared with 12.45 points using AutoMSCs (95%CI: -19.31 to -5.59), thus revealing a significant improvement with AlloMSCs (weighted mean difference: -10.62, 95%CI: -21.23 to -0.11). Additionally, AutoMSCs treatment showed a higher risk of joint-related adverse events (RR = 1.39, 95%CI: 1.07-1.79) compared with AlloMSCs (RR = 1.13, 95%CI: 1.01-1.25). CONCLUSION AlloMSCs may offer superior clinical outcomes with a lower risk of adverse events compared with AutoMSCs in the treatment of KOA. However, the need for further RCTs directly comparing the two MSC types is crucial to validate this data, underscoring the importance of our findings in this field.
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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 | 0.027 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".