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Record W2130722517 · doi:10.1186/s12968-018-0481-8

Guidelines for training in cardiovascular magnetic resonance (CMR)

2018· article· en· W2130722517 on OpenAlexaff
R. J. Kim, Orlando P. Simonetti, Mark Westwood, Christopher M. Kramer, Akhil Narang, Matthias G. Friedrich, Aaron Powell, James Carr, Jeanette Schulz‐Menger, Eike Nagel, Wing-Sze Chan, Jens Bremerich, Karen Ordovás, Robert C. Rollings, Amit R. Patel, Victor A. Ferrari

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsAngiologyMedicineCardiac magnetic resonanceMagnetic resonance imagingNuclear magnetic resonanceCardiologyMedical physicsRadiologyPhysics

Abstract

fetched live from OpenAlex

These "Guidelines for training in Cardiovascular Magnetic Resonance" were developed by the Certification Committee of the Society for Cardiovascular Magnetic Resonance (SCMR) and approved by the SCMR Board of Trustees.

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)0.020
metaresearch head score (Gemma)0.047
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0050.003
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0140.016

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 head0.043
GPT teacher head0.303
Teacher spread0.260 · 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
GenreMethods

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

Citations66
Published2018
Admission routes1
Has abstractyes

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