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Record W2397361021 · doi:10.1097/rli.0000000000000164

Cardiovascular Imaging

2015· review· en· W2397361021 on OpenAlex
Bernd J. Wintersperger, Fabian Bamberg, Carlo N. De Cecco

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInvestigative Radiology · 2015
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of TorontoToronto General Hospital
Fundersnot available
KeywordsModality (human–computer interaction)Magnetic resonance imagingMedicineModalitiesMedical physicsRadiologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Today's noninvasive imaging of the cardiovascular system has revolutionized the approach to various diseases and has substantially affected prognostic information. Cardiovascular magnetic resonance (MR) and computed tomographic (CT) imaging are at center stage of these approaches, although 5 decades ago, these technologies were unheard of. Both modalities had their inception in the 1970s with a primary focus on noncardiovascular applications. The technical development of the various decades, however, substantially pushed the envelope for cardiovascular MR and CT applications. Within the past 10-15 years, MR and CT technologies have pushed each other in cardiac applications; and without the "rival" modality, neither one would likely not have reached its potential today. This view on the history of MR and CT in the field of cardiovascular applications provides insight into the story of success of applications that once have been ideas only but are at prime time today.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.925
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.084
GPT teacher head0.352
Teacher spread0.268 · 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