Clinical efficacy and safety of stem cell therapy for knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: We performed a meta-analysis of the efficacy and safety of stem cell therapy as a clinical treatment of knee osteoarthritis. This meta-analysis is expected to provide evidence of the efficacy of stem cell therapy, which is currently controversial, as a conservative treatment for knee osteoarthritis. METHODS: An online search for relevant articles was conducted in the PubMed, EMBASE, and Cochrane Library databases. The search terms were "stem cells" and "osteoarthritis." We conducted a quality assessment of the included articles and extracted the following indicators: Visual Analogue Scale (VAS) score, Subjective International Knee Documentation Committee (IKDC) score, Western Ontario and McMaster Universities (WOMAC) subscales, and adverse events. The RevMan5.3 software was used for determining effect sizes. RESULTS: Nine randomized controlled trials involving 339 patients were included. VAS score and IKDC score from baseline to 24 months were improved in the stem cell therapy group compared to those in the control group. However, no significant difference was observed between the 2 groups in IKDC score changes from baseline to 6 and 12 months, as well as in WOMAC-Pain, WOMAC-Stiffness, and WOMAC-Physical Function score changes at each visit point. CONCLUSION: Stem cell therapy is certainly superior to traditional treatments in the conservative treatment of KOA; it considerably reduces pain with no obvious additional side effects.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 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".