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Record W4313215425 · doi:10.1016/j.jacbts.2022.08.009

Coronary Flow Reserve, Inflammation, and Myocardial Strain

2022· article· en· W4313215425 on OpenAlexaff
Viviany R. Taqueti, Amil M. Shah, Brendan M. Everett, Aruna D. Pradhan, Gregory Piazza, Courtney Bibbo, Jon Hainer, Victoria L. Morgan, Ana Carolina do A. H. de Souza, Hicham Skali, Ron Blankstein, Sharmila Dorbala, Samuel Z. Goldhaber, Michel R. Le May, Benjamin J.W. Chow, Robert A. deKemp, Fadi G. Hage, Rob Beanlands, Peter Libby, Robert J. Glynn, Scott D. Solomon, Paul M. Ridker, Marcelo F. Di Carli

Bibliographic record

VenueJACC Basic to Translational Science · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsUniversity of Ottawa
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineInflammationCoronary flow reserveCardiologyInternal medicineCoronary artery diseaseHeart failure

Abstract

fetched live from OpenAlex

Inflammation is a key determinant of cardiovascular outcomes, but its role in heart failure is uncertain. In patients with cardiometabolic disease enrolled in the prospective, multicenter ancillary study of CIRT (Cardiovascular Inflammation Reduction Trial), CIRT-CFR (Coronary Flow Reserve to Assess Cardiovascular Inflammation), impaired coronary flow reserve was independently associated with increased inflammation and myocardial strain despite well-controlled lipid, glycemic, and hemodynamic profiles. Inflammation modified the relationship between CFR and myocardial strain, disrupting the association between cardiac blood flow and function. Future studies are needed to investigate whether an early inflammation-mediated reduction in CFR capturing microvascular ischemia may lead to heart failure in patients with cardiometabolic disease. (Cardiovascular Inflammation Reduction Trial [CIRT]; NCT01594333; Coronary Flow Reserve to Assess Cardiovascular Inflammation [CIRT-CFR]; NCT02786134).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.016
GPT teacher head0.255
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations27
Published2022
Admission routes1
Has abstractyes

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