MétaCan
Menu
Back to cohort
Record W4211076845 · doi:10.3390/life12020260

Homologous Use of Allogeneic Umbilical Cord Tissue to Reduce Knee Pain and Improve Knee Function

2022· article· en· W4211076845 on OpenAlexaboutno aff
Ruben Berrocal Timmons, Kimio Sugaya, Lori Deneke Bane

Bibliographic record

VenueLife · 2022
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVisual analogue scaleOsteoarthritisWOMACUmbilical cordAcetaminophenKnee painAnesthesiaPhysical therapy

Abstract

fetched live from OpenAlex

To determine if knee pain subjects who received cryopreserved umbilical cord tissue (UCT) injected into knee joints experience less knee pain, better function, decreased physical limitations, and reduction of medications (opiates, NSAIDs, and acetaminophen) over a 24 week period, Visual Analog Scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and medication usage data were recorded for 30 consenting human knee pain subjects receiving UCT at a single site in the United States. Subject profile information was gathered and analyzed to gain insight into the effects of age, sex, and BMI on improvement over time. Mean resting VAS scores and mean VAS scores with activity improved over 24 weeks (from 1.95 to 0.83 and from 6.28 to 2.87, respectively, p < 0.001). There was no strong evidence of a correlation between sex and VAS scores. There were statistically significant correlations for BMI vs. pre-injection VAS with activity scores and Age vs. pre-injection VAS with activity scores (r = 0.402, p = 0.028 and r = 0.434, p = 0.017, respectively). Mean WOMAC scores improved from 44.7 to 18.5 over 24 weeks (p < 0.001). 77.8% of patients who used medications at the beginning of the study reduced or eliminated medication use. The analysis demonstrates that injections with UCT decrease pain, improve physical function, and allow for less medication use for at least 24 weeks.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.018
GPT teacher head0.275
Teacher spread0.256 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueLifeSame topicKnee injuries and reconstruction techniquesFrench-language works237,207