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Record W4286628835 · doi:10.3389/frym.2022.746884

Can We Heal A Broken Heart With Cells?

2022· article· en· W4286628835 on OpenAlexafffund
Ana Spasojevic, Marc Ruel, Erik J. Suuronen, Emilio I. Alarcón

Bibliographic record

VenueFrontiers for Young Minds · 2022
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Ottawa
FundersMinistero dello Sviluppo EconomicoCanadian Institutes of Health ResearchUniversity of OttawaNatural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsStem cellMyocyteHeart cellsHuman heartHeart beatMedicineBeat (acoustics)CardiologyInternal medicineCell biologyBiology

Abstract

fetched live from OpenAlex

Cardiomyocytes are the muscle cells that make the heart beat, pump oxygen, and deliver nutrient-rich blood throughout the human body. During a heart attack, the blood supply to the heart is interrupted. Cardiomyocytes then die and are replaced by scar tissue that can no longer contract. As a result, the heart is weakened and may beat abnormally. For many years, researchers have been searching for a way to replace damaged cardiomyocytes with new ones. Stem cells are master cells that grow and divide rapidly. They may be ideal for repairing organs and tissues because they can turn into many different cell types, including cardiomyocytes. Among other medical therapies, stem cells have been used to develop the cardiac patch, a heart “band-aid” that can regenerate damaged heart muscle. In this article, we will discuss the advantages and limitations of using stem cells for repairing a “broken heart.”

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.481

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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