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Record W4399390053 · doi:10.1016/j.jcct.2024.05.232

Standards for quantitative assessments by coronary computed tomography angiography (CCTA)

2024· article· en· W4399390053 on OpenAlexaff
Koen Nieman, Alexandre Hideo‐Kajita, Carlos Collet, Damini Dey, Francesca Pugliese, Gaby Weissman, Jan G.P. Tijssen, Jonathon Leipsic, Maksymilian P Opolski, Maros Ferencik, Michael T Lu, Michelle C. Williams, Nico Bruining, Pal Maurovich-Horvat, Stephan Achenbach

Bibliographic record

VenueJournal of cardiovascular computed tomography · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of British Columbia
FundersNational Heart, Lung, and Blood InstituteSiemens HealthineersKowa CompanySiemens Medical Solutions USACedars-Sinai Medical CenterNational Institutes of HealthBoston Scientific CorporationAstraZenecaAmerican Heart Association
KeywordsMedicineCoronary artery diseaseRadiologyCoronary angiographyClinical PracticeCardiac imagingComputed tomography angiographyAngiographyQuantitative assessmentTomographyComputed tomographyCardiologyInternal medicineMyocardial infarctionRisk analysis (engineering)

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.105
metaresearch head score (Gemma)0.199
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.895
Threshold uncertainty score0.555

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1050.199
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0100.005
Science and technology studies0.0050.005
Scholarly communication0.0100.004
Open science0.0120.007
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0050.008

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 head0.010
GPT teacher head0.267
Teacher spread0.257 · 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.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations94
Published2024
Admission routes1
Has abstractno

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