MétaCan
Menu
Back to cohort
Record W4289774727 · doi:10.1097/jsa.0000000000000350

Allogenic Umbilical Cord Tissue for Treatment of Knee Osteoarthritis

2022· article· en· W4289774727 on OpenAlexaboutno aff
Ashim Gupta, Nicola Maffulli

Bibliographic record

VenueSports Medicine and Arthroscopy Review · 2022
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisUmbilical cordSurgeryPhysical therapyAnatomyPathologyAlternative medicine

Abstract

fetched live from OpenAlex

Interest in use of perinatal allogenic tissues including clinical-grade minimally manipulated umbilical cord tissue-derived allograft formulations to treat knee osteoarthritis (OA) patients is increasing. Limited studies have characterized these formulations and evaluated their safety and efficacy in knee OA patients. We developed such formulation and reported the presence of growth factors, cytokines, hyaluronic acid, and exosomes. We reported that its administration is safe, and resulted in 50% pain reduction and improvement in knee injury and osteoarthritis outcome score (over 10%) and 36-item short form survey (25%). Another study reported no adverse events post injection of similar formulation and statistically significant ( P <0.001) improvement in visual analog scale and Western Ontario and McMaster Universities Osteoarthritis Index scores and reduction in medication usage in patients (77.8%). We also summarized the clinical trials registered on ClinicalTrials.gov utilizing umbilical cord tissue for knee OA treatment. In conclusion, available studies are preliminary but pave the way to higher level appropriately powered investigations, and these formulations should be considered as nonoperative alternative to manage knee OA.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.339
Teacher spread0.309 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations19
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueSports Medicine and Arthroscopy ReviewSame topicOsteoarthritis Treatment and MechanismsFrench-language works237,207