MétaCan
Menu
Back to cohort
Record W2397361021 · doi:10.1097/rli.0000000000000164

Cardiovascular Imaging

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

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.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0300.024

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

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

Citations27
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInvestigative RadiologySame topicCardiac Imaging and DiagnosticsFrench-language works237,207