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Record W2034332108 · doi:10.1186/1532-429x-14-s1-p179

The diagnostic performance of non-contrast T1-mapping in patients with acute myocarditis on cardiovascular magnetic resonance imaging

2012· article· en· W2034332108 on OpenAlexafffund
Vanessa M. Ferreira, Stefan K. Piechnik, Erica Dall’Armellina, Theodoros D. Karamitsos, Jane M Francis, Robin P. Choudhury, Attila Kardos, Matthias G. Friedrich, Matthew D. Robson, Stefan Neubauer

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2012
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversité de MontréalUniversity of Calgary
FundersClarendon FundAlberta Heritage Foundation for Medical ResearchFondation pour la Recherche MédicaleUniversity of OxfordNational Institute for Health and Care ResearchNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchBritish Heart FoundationWellcome Trust
KeywordsAngiologyMedicineMyocarditisAcute myocarditisMagnetic resonance imagingContrast (vision)Cardiac magnetic resonanceRadiologyDiagnostic accuracyContrast enhancementCardiac magnetic resonance imagingCardiologyInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

Non-contrast T1-mapping using ShMOLLI can serve as a novel CMR diagnostic criterion in patients presenting with suspected acute myocarditis.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.216
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2012
Admission routes2
Has abstractyes

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