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Stem cell injections for osteoarthritis of the knee

2025· review· en· W4409119789 on OpenAlexaff
Samuel Whittle, Renea V Johnston, Steve McDonald, Daniel L. Worthley, T. Mark Campbell, Sheila Cyril, Tanay Bapna, Jason Zhang, Rachelle Buchbinder

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2025
Typereview
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsÉlisabeth Bruyère Hospital
Fundersnot available
KeywordsMedicineOsteoarthritisPlaceboClinical trialAdverse effectPhysical therapyInternal medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Stem cells are specialised precursor cells that can replace aged or damaged cells and thereby maintain healthy tissue function. Stem cell therapy is increasingly used as a treatment for knee osteoarthritis, despite the lack of clarity around the mechanism by which stem cell therapy may slow down disease progression in osteoarthritis, and uncertainty regarding its benefits and harms. OBJECTIVES: To assess the benefits and harms of stem cell injections for people with osteoarthritis of the knee. A secondary objective is to maintain the currency of the evidence, using a living systematic review approach. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE and Embase on 15 September 2023, unrestricted by date or language of publication. We also searched ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform (ICTRP) for relevant trial protocols and ongoing trials. SELECTION CRITERIA: We included randomised controlled trials (RCTs), or trials using quasi-randomised methods of participant allocation, comparing stem cell injection with placebo injection, no treatment or usual care, glucocorticoid injection, other injections, exercise, drug therapy, surgical interventions, and supplements and complementary therapies in people with knee osteoarthritis. DATA COLLECTION AND ANALYSIS: Two review authors selected studies for inclusion, extracted trial characteristics and outcome data, assessed risk of bias and assessed the certainty of evidence using the GRADE approach. The primary comparison was stem cell injection compared with placebo injection. The primary time point for pain, function and quality of life was three to six months, and the end of the trial period for participant-reported success, joint structure changes and adverse event outcomes. Major outcomes were pain, function, quality of life, global assessment of success, radiographic joint progression, withdrawals due to adverse events and serious adverse events. MAIN RESULTS: = 0%; 7 trials, 461 participants) and there were no reported withdrawals due to adverse events. We downgraded the evidence to very low certainty due to indirectness, suspected publication bias and imprecision. Radiographic progression was not assessed in any of the included studies. AUTHORS' CONCLUSIONS: Compared with placebo injections and based upon low-certainty evidence, stem cell injections for people with knee osteoarthritis may slightly improve pain and function. We are uncertain of the effects of stem cell injections on quality of life or the number who report treatment success. Although the putative benefits of stem cell therapies for osteoarthritis include potential regenerative effects on damaged tissues, particularly articular cartilage, we remain uncertain of the effect of stem cell injections on structural progression in the knee (measured by radiographic appearance). There is also uncertainty regarding the safety of stem cell injections. Serious adverse events were infrequently reported, although all invasive joint procedures (including injections) carry a small risk of septic arthritis. The risk of other important harms, including potential concerns related to the use of a therapy with the theoretical capacity to promote cell growth, or to the use of allogeneic cells, remains unknown.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0050.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.137
GPT teacher head0.412
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

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Citations16
Published2025
Admission routes1
Has abstractyes

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