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Record W2575784697 · doi:10.4252/wjsc.v9.i1.9

Optimizing stem cells for cardiac repair: Current status and new frontiers in regenerative cardiology

2017· review· en· W2575784697 on OpenAlexaff
Shant Der Sarkissian, Thierry Lévesque, Nicolas Noiseux

Bibliographic record

VenueWorld Journal of Stem Cells · 2017
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsStem cellRegenerative medicineMedicineStem-cell therapyCell therapyParacrine signallingPopulationAngiogenesisNeuroscienceBioinformaticsCancer researchBiologyInternal medicineCell biology

Abstract

fetched live from OpenAlex

Cell therapy has the potential to improve healing of ischemic heart, repopulate injured myocardium and restore cardiac function. The tremendous hope and potential of stem cell therapy is well understood, yet January 26, 2017|Volume |Issue 1| WJSC|www.wjgnet.com Der Sarkissian S et al . Optimizing stem cells for regenerative cardiology strategies that have been used to optimize and increase survival and functionality of stem cells including ex vivo preconditioning with drugs and a novel "pharmacooptimizer" as well as genetic modifications.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.087
GPT teacher head0.342
Teacher spread0.255 · 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 designNot applicable
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

Citations53
Published2017
Admission routes1
Has abstractyes

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