Lights and shadows of cardiac magnetic resonance imaging in acute myocarditis
Bibliographic record
Abstract
UNLABELLED: Cardiac magnetic resonance (CMR) is considered a primary tool for the diagnosis of acute myocarditis, due to its unique potential for non-invasive identification of the various hallmarks of the inflammatory response, with relevant impact on patient management and prognosis. Nonetheless, a marked variation in sensitivity and negative predictive value has been reported in the literature, reflecting the intrinsic drawbacks of current diagnostic criteria, which are based mainly on the use of conventional CMR pulse sequences. As a consequence, a negative exam cannot reliably exclude the diagnosis, especially in patients who do not present an infarct-like onset of disease. The introduction of new-generation mapping techniques further widened CMR potentials, allowing quantification of tissue changes and opening new avenues for non-invasive workup of patients with inflammatory myocardial disease. MAIN MESSAGES: • CMR sensitivity varies in AM, reflecting its clinical polymorphism and the intrinsic drawbacks of LLc. • Semiquantitative approaches such as EGEr or T2 ratio have limited accuracy in diffuse disease forms. • T1 mapping allows objective quantification of inflammation, with no need to normalize measurements. • A revised protocol including T2-STIR, T1 mapping and LGE could be hypothesized to improve sensitivity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
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".