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Record W4392855169 · doi:10.1177/08465371241233228

CAR and CSTR Cardiac Computed Tomography (CT) Practice Guidelines: Part 2-Non-Coronary Imaging

2024· review· en· W4392855169 on OpenAlexaffabout
Elsie T. Nguyen, Courtney R. Green, Scott Adams, Helen Bishop, Guylaine Gleeton, Cameron Hague, Kate Hanneman, Scott R. Harris, Jacek Strzelczyk, Carole Dennie

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2024
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of OttawaUniversity of ManitobaUniversity of British ColumbiaMemorial University of NewfoundlandRoyal University HospitalInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité LavalUniversity of SaskatchewanDalhousie UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineRadiologyCardiac imagingComputed tomographyClinical PracticeTomographyCoronary angiographyMedical physicsCardiology

Abstract

fetched live from OpenAlex

The cardiac computed tomography (CT) practice guidelines provide an updated review of the technological improvements since the publication of the first Canadian Association of Radiologists (CAR) cardiac CT practice guidelines in 2009. An overview of the current evidence supporting the use of cardiac CT in the most common clinical scenarios, standards of practice to optimize patient preparation and safety as well as image quality are described. Coronary CT angiography (CCTA) is the focus of Part I. In Part II, an overview of cardiac CT for non-coronary indications that include valvular and pericardial imaging, tumour and mass evaluation, pulmonary vein imaging, and imaging of congenital heart disease for diagnosis and treatment monitoring are discussed. The guidelines are intended to be relevant for community hospitals and large academic centres with established cardiac CT imaging programs.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.006

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 head0.032
GPT teacher head0.345
Teacher spread0.313 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Association of Radiologists JournalSame topicCardiac Imaging and DiagnosticsFrench-language works237,207