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Record W4283454059 · doi:10.5772/intechopen.105498

Cardiovascular Magnetic Resonance Imaging of Atrial Fibrillation: An Advanced Hemodynamic Perspective

2022· book-chapter· en· W4283454059 on OpenAlex
Mankarman Ghuman, Han‐Suk Kim, Hana Sheitt, Julio García

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIntechOpen eBooks · 2022
Typebook-chapter
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of Calgary
FundersSiemens HealthineersUniversity of Calgary
KeywordsCardiologyMedicineInternal medicineAtrial fibrillationMagnetic resonance imagingVentricleHemodynamicsThrombusBlood flowRadiology

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) patients can be referred to cardiac magnetic resonance imaging (MRI) for an accurate assessment of cardiac function and left atrial structure. Cardiac MRI is the gold standard for the quantification of heart volumes and allows the noninvasive tissue characterization of the heart. In addition, advanced flow assessment can be achieved using 4D-flow MRI to elegantly depict the hemodynamic efficiency of the left atrium (LA) and left ventricle (LV) throughout the cardiac cycle. Patients with AF may have occult LV disease and thrombus formation. Biomarkers based on 4D-flow MRI may unmask the presence of LA/LV disease by quantifying 3D stasis, flow distribution, and vortex formation. These biomarkers have proved to characterize AF stages, to complement standard risk scores, and bring new insights on heart hemodynamic performance. This chapter aims to present a standard cardiac MRI protocol for atrial fibrillation and the innovative usefulness of advanced flow imaging in clinical settings.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0020.000

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 head0.027
GPT teacher head0.294
Teacher spread0.267 · 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