Noninvasive imaging in acute myocarditis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".