Efficacy of mesenchymal stem cells in treating patients with osteoarthritis of the knee: A meta-analysis
Bibliographic record
Abstract
To assess the clinical efficacy and safety of mesenchymal stem cell (MSC) treatment for osteoarthritis of the knee (KOA), a systematic electronic literature search was performed on PubMed, EMBASE and Web of Science. Studies published in English from the earliest record to December 2014 were searched using the following keywords: Cartilage defect, cartilage repair, osteoarthritis, KOA, stem cells, MSCs, bone marrow concentrate (BMC), adipose-derived mesenchymal stem cells, synovial-derived mesenchymal stem cells and peripheral blood-derived mesenchymal stem cells. The effect sizes of selected studies were determined by extracting pain scores from the visual analog scale and functional changes from International Knee Documentation Committee and Lysholm and Western Ontario and McMaster Universities Osteoarthritis Index before and after MSCs or reference treatments at 3, 6, 12, and 24 months. The factors were analyzed and the outcomes were modified after comparing the MSC group pooled values with the pretreatment baseline or between different treatment arms. A systematic search identified 18 clinical trials on this topic, including 10 single-arm prospective studies, four quasi-experimental studies and four randomized controlled trials that used BMCs to treat 565 patients with KOA in total. MSC treatment in patients with KOA showed continual efficacy for 24 months compared with their pretreatment condition. Effectiveness of MSCs was improved at 12 and 24 months post-treatment, compared with at 3 and 6 months. No dose-responsive association in the MSCs numbers was demonstrated. However, patients with arthroscopic debridement, activation agent or lower degrees of Kellgren-Lawrence grade achieved improved outcomes. MSC application ameliorated the overall outcomes of patients with KOA, including pain relief and functional improvement from basal evaluations, particularly at 12 and 24 months after follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".