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Record W2120901081 · doi:10.1186/s12968-014-0103-z

Simplifying cardiovascular magnetic resonance pulse sequence terminology

2014· editorial· en· W2120901081 on OpenAlexaff
Matthias G. Friedrich, Chiara Bucciarelli‐Ducci, James A. White, Sven Plein, James Moon, Ana G. Almeida, Christopher M. Kramer, Stefan Neubauer, Dudley J. Pennell, Steffen E. Petersen, Raymond Y. Kwong, Victor A. Ferrari, Jeanette Schulz‐Menger, Hajime Sakuma, Erik B. Schelbert, Éric Larose, Ingo Eitel, Iacopo Carbone, Andrew J. Taylor, Alistair A. Young, Albert de Roos, Eike Nagel

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2014
Typeeditorial
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsInstitut Universitaire de Cardiologie et de Pneumologie de QuébecUniversité LavalLibin Cardiovascular Institute of AlbertaMontreal Heart InstituteUniversité de Montréal
FundersBritish Heart Foundation
KeywordsTerminologySequence (biology)MedicineCLARITYSet (abstract data type)Meaning (existential)Computer scienceMedical physicsData sciencePsychologyLinguistics

Abstract

fetched live from OpenAlex

We propose a set of simplified terms to describe applied Cardiovascular Magnetic Resonance (CMR) pulse sequence techniques in clinical reports, scientific articles and societal guidelines or recommendations. Rather than using various technical details in clinical reports, the description of the technical approach should be based on the purpose of the pulse sequence. In scientific papers or other technical work, this should be followed by a more detailed description of the pulse sequence and settings. The use of a unified set of widely understood terms would facilitate the communication between referring physicians and CMR readers by increasing the clarity of CMR reports and thus improve overall patient care. Applied in research articles, its use would facilitate non-expert readers' understanding of the methodology used and its clinical meaning.

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)0.005
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.536
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.010
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

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

Citations11
Published2014
Admission routes1
Has abstractyes

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