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Record W4399506346 · doi:10.5144/0256-4947.2024.195

Mesenchymal stem cells and platelet rich plasma therapy for knee osteoarthritis: an umbrella review of systematic reviews with meta-analysis

2024· review· en· W4399506346 on OpenAlexaboutno aff
Feng Lin, Xinguang Zhang, Cunbao Cui

Bibliographic record

VenueAnnals of Saudi Medicine · 2024
Typereview
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoarthritisMedicineMesenchymal stem cellPlatelet-rich plasmaCochrane LibraryMeta-analysisSystematic reviewMagnetic resonance imagingPhysical therapyMEDLINEInternal medicineBioinformaticsPathologyPlateletAlternative medicineRadiology

Abstract

fetched live from OpenAlex

The effect of mesenchymal stem cells (MSCs) and platelet-rich plasma (PRP) therapy on knee osteoarthritis (KOA) has been contradictory in previous meta-analyses. This umbrella review on published meta-analyses aimed to investigate the effect of MSCs and PRP on KOA. We systematically searched Scopus, PubMed, and Cochrane databases to include related meta-analyses. The outcome included studies reporting visual analog scale scores, the Western Ontario and McMaster Universities Osteoarthritis Index, Whole-Organ Magnetic Resonance Imaging Scores, International Knee Documentation Committee scores, and the Knee injury and Osteoarthritis Outcome Score. A total of 28 meta-analyses with 32 763 participants. MSCs and PRP therapies were significantly associated with an improvement in KOA scores. This umbrella meta-analysis supports the beneficial health effects of MSCs and PRP in KOA.

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)0.015
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.044
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0160.021
Bibliometrics0.0100.011
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.363
GPT teacher head0.444
Teacher spread0.082 · 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 designMeta-analysis
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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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