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Record W3046935052 · doi:10.2967/jnumed.120.246280

Appropriate Use Criteria for PET Myocardial Perfusion Imaging

2020· review· en· W3046935052 on OpenAlexaff
Thomas H. Schindler, Timothy M. Bateman, Daniel S. Berman, Panithaya Chareonthaitawee, Lorraine E. De Blanche, Vasken Dilsizian, Sharmila Dorbala, Robert J. Gropler, Leslee J. Shaw, Prem Soman, David E. Winchester, Hein J. Verberne, Sukhjeet Ahuja, Rob Beanlands, Marcelo F. Di Carli, Venkatesh L. Murthy, Terrence D. Ruddy, Ronald G. Schwartz

Bibliographic record

VenueJournal of Nuclear Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsCanadian Cardiovascular Society
Fundersnot available
KeywordsMyocardial perfusion imagingCoronary artery diseaseMedicineRisk stratificationCardiologyPerfusionInternal medicinePerfusion scanningRadiologyCAD

Abstract

fetched live from OpenAlex

In the last decade, myocardial perfusion imaging (MPI) with PET has emerged to play a pivotal role in the clinical routine process for the detection of hemodynamically significant obstructive coronary artery disease (CAD) and cardiovascular risk stratification ([1][1]–[5][2]). The high spatial and

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)0.002
metaresearch head score (Gemma)0.005
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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.006

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 head0.059
GPT teacher head0.369
Teacher spread0.310 · 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

Citations59
Published2020
Admission routes1
Has abstractyes

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