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Record W2418080393 · doi:10.1097/hco.0000000000000265

Noninvasive imaging in acute myocarditis

2016· review· en· W2418080393 on OpenAlexafffund
Karan Bami, Tony Haddad, Alexander Dick, Carole Dennie, Girish Dwivedi

Bibliographic record

VenueCurrent Opinion in Cardiology · 2016
Typereview
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsUniversity of OttawaOttawa Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineMyocarditisAcute myocarditisCardiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The gold standard for diagnosing acute myocarditis is endomyocardial biopsy, but it is highly invasive and can result in serious complications. Noninvasive imaging has an essential role in the management of suspected acute myocarditis. In this article, we aim to review the role of various imaging techniques in acute myocarditis. RECENT FINDINGS: Newer methods such as strain and strain rate imaging using speckle tracking have emerged as an adjunctive echocardiographic parameter of myocardial dysfunction. The latest advancements in cardiovascular magnetic resonance (CMR) techniques have allowed quantitative T1 and T2 mappings that aim to quantify the areas of edematous myocardium and also address some of the limitations of traditional techniques as viable tools. An automated method for calculating late gadolinium enhancement by CMR has been developed in recent years. 18-Fluorodeoxyglucose PET is increasingly being used to assist in the diagnosis of myocarditis associated with cardiac sarcoidosis. SUMMARY: Echocardiography remains an essential and most commonly used initial investigation in suspected myocarditis. Due to the recent technological hardware and software advancements in CMR technology, CMR continues to occupy a pole position amongst all the other imaging modalities. The utility of cardiac computed tomography is less clear.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.118
GPT teacher head0.465
Teacher spread0.347 · 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 designOther design
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

Citations28
Published2016
Admission routes2
Has abstractyes

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