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Record W2522091447 · doi:10.1186/s13550-016-0225-4

82Rb PET imaging of myocardial blood flow—have we achieved the 4 “R”s to support routine use?

2016· editorial· en· W2522091447 on OpenAlexaff
Robert A. deKemp, Ran Klein, Rob Beanlands

Bibliographic record

VenueEJNMMI Research · 2016
Typeeditorial
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineCardiac PETPositron emission tomographyCardiac imagingBlood flowNuclear medicinePet imagingMyocardial perfusion imagingRadiologyCoronary flow reserveCardiologyInternal medicineMedical physicsCoronary artery disease

Abstract

fetched live from OpenAlex

Routine quantification of myocardial blood flow (MBF) in absolute units of mL/min/g has long been one of the technical milestones expected to enable the widespread clinical application of cardiac positron emission tomography (PET). The recent study by Germino et al. [1] contributes significantly to the increasing body of literature supporting the potential for rubidium-82 (82Rb) PET to become the de facto clinical standard method for non-invasive quantification of MBF and myocardial flow reserve (MFR) in the routine diagnosis and management of patients with ischemic heart disease. The authors are commended for completing a very technically challenging study, including three-dimensional PET parametric imaging and arterial blood sampling validation with the ultra-short-lived tracers 82Rb and 15O-water.

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 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)0.006
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.040
GPT teacher head0.391
Teacher spread0.352 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations13
Published2016
Admission routes1
Has abstractyes

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