Mesenchymal stem cells - a promising strategy for treating knee osteoarthritis
Bibliographic record
Abstract
Aims The purpose of our study was to determine whether mesenchymal stem cells (MSCs) are an effective and safe therapeutic agent for the treatment of knee osteoarthritis (OA), owing to their cartilage regeneration potential. Methods We searched PubMed, Embase, and the Cochrane Library, with keywords including “knee osteoarthritis” and “mesenchymal stem cells”, up to June 2019. We selected randomized controlled trials (RCTs) that explored the use of MSCs to treat knee OA. The visual analogue scale (VAS), Western Ontario and McMaster University Osteoarthritis Index (WOMAC), adverse events, and the whole-organ MRI score (WORMS) were used as the primary evaluation tools in the studies. Our meta-analysis included a subgroup analysis of cell dose and cell source. Results Seven trials evaluating 256 patients were included in the meta-analysis. MSC treatment significantly improved the VAS (mean difference (MD), –13.24; 95% confidence intervals (CIs) –23.28 to –3.20, p = 0.010) and WOMAC (MD, –7.22; 95% CI –12.97 to –1.47, p = 0.010). The low-dose group with less than 30 million cells showed lower p-values for both the VAS and WOMAC. Adipose and umbilical cord–derived stem cells also had lower p-values for pain scores than those derived from bone marrow. Conclusion Overall, MSC-based cell therapy is a relatively safe treatment that holds great potential for OA, evidenced by a positive effect on pain and knee function. Using low-dose (25 million) and adipose-derived stem cells is likely to achieve better results, but further research is needed. Cite this article: Bone Joint Res 2020;9(10):719–728.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".