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Record W2519403959

Systemic and inflammatory disorders involving the heart: the role of PET imaging.

2016· article· en· W2519403959 on OpenAlexaff
Daniel Juneau, Fernanda Erthal, Atif Alzahrani, Ali Alenazy, Pablo B. Nery, Rob Beanlands, Benjamin J.W. Chow

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicPericarditis and Cardiac Tamponade
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineMyocarditisPericarditisPositron emission tomographyCardiac imagingPet imagingEndocarditisRadiologyCardiac amyloidosisFluorodeoxyglucoseAmyloidosisCardiologyPathology
DOInot available

Abstract

fetched live from OpenAlex

Cardiac inflammatory disorders, either primarily cardiac or secondary to a systemic process, are associated with significant morbidity and/or mortality. Their diagnosis can be challenging, especially due to significant overlap in their clinical presentation with other cardiac diseases. Recent publications have investigated the potential diagnostic role of positron emission tomography (PET) imaging in these patients. Most of the available literature is focused on Fluorine-18 fluorodeoxyglucose (FDG), a tracer which has already demonstrated its use in other inflammatory and infectious processes. PET imaging can help in the diagnosis, prognosis and follow-up in a variety of cardiac inflammatory processes, including infective endocarditis, cardiac implantable electronic device infection, pericarditis, myocarditis, sarcoidosis and amyloidosis. PET's ability to depict metabolic changes and abnormalities, sometime even before the onset of any anatomical changes, can be a significant advantage over standard anatomical imaging. PET appears to be particularly useful in cases where standard investigation is non-diagnostic or equivocal.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.115

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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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 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

Citations15
Published2016
Admission routes1
Has abstractyes

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