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Integrating perfusion with AI–derived coronary calcium on CT attenuation scans to improve selection of low-risk studies for stress-only SPECT myocardial perfusion imaging

2025· article· en· W4414107131 on OpenAlexaff
Robert J.H. Miller, Orit Barrett, Aakash Shanbhag, Alan Rozanski, Damini Dey, Mark Lemley, Serge D. Van Kriekinge, Paul Kavanagh, Attila Fehér, Edward J. Miller, Andrew J. Einstein, Terrence D. Ruddy, Timothy Bateman, Philipp A. Kaufmann, Joanna X. Liang, Daniel S. Berman, Piotr J. Slomka

Bibliographic record

VenueJournal of Nuclear Cardiology · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of OttawaArtificial Intelligence in Medicine (Canada)
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthDr. Miriam and Sheldon G. Adelson Medical Research FoundationAmerican College of Cardiology Foundation
KeywordsPerfusionCorrection for attenuationMyocardial perfusion imagingPerfusion scanningSelection (genetic algorithm)Coronary artery calcium

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 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)0.001
metaresearch head score (Gemma)0.002
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 score0.263
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.302
Teacher spread0.290 · 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

Citations4
Published2025
Admission routes1
Has abstractno

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