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Record W1983200035 · doi:10.1517/14712598.2013.748739

Cell-based vasculogenic studies in preclinical models of chronic myocardial ischaemia and hibernation

2012· review· en· W1983200035 on OpenAlexafffund
Céline Giordano, Drew Kuraitis, Rob Beanlands, Erik J. Suuronen, Marc Ruel

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsMedicineCardiologyIschemiaCoronary artery diseaseInternal medicineHibernation (computing)PopulationHibernating myocardiumMyocardial infarctionRevascularizationComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: Coronary artery disease commonly leads to myocardial ischaemia and hibernation. Relevant preclinical models of these conditions are essential to evaluate new therapeutic options such as cell-based vasculogenic therapies. AREAS COVERED: In this article, the authors first review basic concepts of myocardial ischaemia/hibernation and relevant techniques to assess myocardial viability. Then, preclinical models of chronic myocardial ischaemia and hibernation, induced by devices such as ameroid constrictors, Delrin stenosis, hydraulic occluders, and coils/stents are described. Lastly, the authors discuss cell-based vasculogenic therapy, and summarise studies conducted in large animal models of chronic myocardial ischaemia and hibernation. EXPERT OPINION: Approximately one-third of patients with viable myocardium do not undergo revascularisation; however, this population is at high risk for cardiac events and would surely benefit from effective cell-based therapy. Because of the modest benefits in clinical studies, preclinical models accurately representing clinical myocardial ischemia/hibernation are necessary to better understand and appropriately direct regenerative therapy research.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.362
GPT teacher head0.486
Teacher spread0.124 · 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.

Study designOther design
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

Citations10
Published2012
Admission routes2
Has abstractyes

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