Effect of mesenchymal stromal cells transplantation on the outcomes of patients with knee osteoarthritis: A systematic review and meta‐analysis
Bibliographic record
Abstract
Cell therapy has been explored as a new regenerative treatment for osteoarthritis in the field of regenerative medicine. However, the efficacy of stem cell transplantation from different sources for the treatment of knee osteoarthritis (KOA) remains controversial. This study integrates and evaluates the previously published data of stem cell transplantation for KOA to explore the curative effect of different stem cells. We conducted a meta-analysis of randomized controlled trials on stem cell therapy for KOA. Measures of efficacy included Visual Analog Scale (VAS), Lequesne index, Lysholm Knee Scoring Scale (LKSS), and Western Ontario and McMaster University Osteoarthritis Index (WOMAC). Joint injury was evaluated through the Whole-Organ Magnetic Resonance Imaging Score (WORMS) system. We analyzed 16 studies involving 875 KOA patients. The stem cell treatment showed significant VAS reduction from the third month onwards. Subgroup analysis suggested the most significant pain relief at different postoperative months came from adipose-derived and umbilical cord-derived stem cells. Autologous adipose tissue resulted in better pain alleviation compared with allogenic. However, autologous bone marrow stem cells did not show increased pain relief over allogeneic ones. Combination therapy (HA and/or PRP) showed no effect. Autologous adipose-derived stem cells demonstrate the most effective recovery of knee joint function. In WORMS assessment, there was no significant difference between the stem cell group and control. Stem cell transplantation proved safe and effective for KOA treatment. Different sources stem cells have a good effect on alleviating knee joint pain, restoring knee joint function, and minimizing patient trauma.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.024 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".