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Record W2564829560 · doi:10.14740/cr485w

Diagnostic Approach to Myocarditis Mimicking Myocardial Infarction at Initial Presentation

2016· article· en· W2564829560 on OpenAlexvenueno aff
Craig Basman, Pratik R Agrawal, Chad McRee, Louis D. Saravolatz, Carol Chen‐Scarabelli, Tiziano M. Scarabelli

Bibliographic record

VenueCardiology Research · 2016
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMyocarditisCardiologyCardiac catheterizationInternal medicineMyocardial infarctionEjection fractionChest painCoronary arteriesNauseaCardiac magnetic resonance imagingMagnetic resonance imagingVomitingRadiologyHeart failureArtery

Abstract

fetched live from OpenAlex

We present a case of a 35-year-old male patient with a 12-hour history of sudden-onset, crushing chest pain and associated complaints of profuse diaphoresis, nausea and vomiting. The patient was transferred to our institution from an outside hospital for evaluation and possible emergent catheterization. Left heart catheterization was conclusive for normal coronary arteries and a ventriculogram revealed a left ventricular ejection fraction of approximately 45%. Due to a suspicion of myocarditis based on clinical history, pertinent serology tests were ordered, which were found to be negative. Cardiac magnetic resonance on delayed enhancement imaging showed typical sub-epicardial enhancement in a pattern most consistent with myocarditis. The patient was eventually diagnosed with myocarditis and discharged home later, without needing a myocardial biopsy. We present and discuss here the indications of myocardial biopsy and compare the relative utility of cardiac magnetic resonance imaging in formulating the diagnosis of 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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.114
GPT teacher head0.418
Teacher spread0.304 · 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.

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

Citations8
Published2016
Admission routes1
Has abstractyes

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