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Record W2795450295 · doi:10.3390/bioengineering5020028

Therapeutic Use of Stem Cells for Myocardial Infarction

2018· review· en· W2795450295 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioengineering · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsHealth Sciences NorthNOSM University
Fundersnot available
KeywordsMyocardial infarctionStem cellMedicineCardiologyInternal medicineIntensive care medicineBiologyCell biology

Abstract

fetched live from OpenAlex

Myocardial infarction is a leading cause of morbidity and mortality worldwide. Although medical and surgical treatments can significantly improve patient outcomes, no treatment currently available is able to generate new contractile tissue or reverse ischemic myocardium. Driven by the recent/novel understanding that regenerative processes do exist in the myocardium-tissue previously thought not to possess regenerative properties-the use of stem cells has emerged as a promising therapeutic approach with high expectations. The literature describes the use of cells from various sources, categorizing them as either embryonic, induced pluripotent, or adult/tissue stem cells (mesenchymal, hematopoietic, skeletal myoblasts, cardiac stem cells). Many publications show the successful use of these cells to regenerate damaged myocardium in both animal and human models; however, more studies are needed to directly compare cells of various origins in efforts to draw conclusions on the ideal source. Although numerous challenges exist in this developing area of research and clinical practice, prospects are encouraging. The following aims to provide a concise review outlining the different types of stem cells used in patients after myocardial infarction.

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.

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: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.074
GPT teacher head0.314
Teacher spread0.240 · 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