No evidence for the use of stem cell therapy for tendon disorders: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Stem cells have emerged as a new treatment option for tendon disorders. We systematically reviewed the current evidence for stem cell therapy in tendon disorders. METHODS: Randomised and non-randomised controlled trials, cohort studies and case series with a minimum of 5 cases were searched in MEDLINE, CENTRAL, EMBASE, CINAHL, PEDro and SPORTDiscus. In addition, we searched grey literature databases and trial registers. Only human studies were included and no time or language restrictions were applied to our search. All references of included trials were checked for possibly eligible trials. Risk of bias assessment was performed using the Cochrane risk of bias tool for controlled trials and the Newcastle-Ottawa scale for case series. Levels of evidence were assigned according to the Oxford levels of evidence. RESULTS: 4 published and three unpublished/pending trials were found with a total of 79 patients. No unpublished data were available. Two trials evaluated bone marrow-derived stem cells in rotator cuff repair surgery and found lower retear rates compared with historical controls or the literature. One trial used allogenic adipose-derived stem cells to treat lateral epicondylar tendinopathy. Improved Mayo Elbow Performance Index, Visual Analogue Pain scale and ultrasound findings after 1-year follow-up compared with baseline were found. Bone marrow-derived stem cell-treated patellar tendinopathy showed improved International Knee Documentation Committee, Knee injury and Osteoarthritis Outcome Score subscales and Tegner scores after 5-year follow-up. One trial reported adverse events and found them to be mild (eg, swelling, effusion). All trials were at high risk of bias and only level 4 evidence was available. CONCLUSIONS: No evidence (level 4) was found for the therapeutic use of stem cells for tendon disorders. The use of stem cell therapy for tendon disorders in clinical practice is currently not advised.
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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 | 0.021 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".