CMR techniques and findings in children with myocarditis: a multicenter retrospective study
Bibliographic record
Abstract
Background CMR is increasingly used to diagnose myocarditis in adults but its use in pediatric-age pts is not well estab- lished. We sought to describe the clinical presentation, CMR imaging protocols, CMR findings, and outcomes in a multicenter cohort of children with myocarditis. Methods 71 studies (66%) and was abnormal in 49 (69%). First pass contrast perfusion (FPP) imaging was performed in 42 studies (45%) and was abnormal in 4 (10%). T1- weighted imaging for early gadolinium enhancement (EGE) was performed in 35 studies (37%) and was abnor- mal in 19 (51%). Late gadolinium enhancement (LGE) imaging was performed in all studies, and was abnormal in 93 (83%) with the following reported distributions: 89% subepicardial or midwall, 6% patchy, 3% sub-endo- cardial, 1% transmural, and 1% diffuse. The CMR study was interpreted as positive for myocarditis in 96 pts (87%), negative in 11 (10%), and equivocal in 4 (4%), yielding a sensitivity of 86% for an ultimate clinical diag- nosis of myocarditis. There was significant practice varia- tion in the use of T2W, FPP, and EGE imaging among the participating institutions (Figure 1). At a median fol- low-up time from CMR of 6 mo (0.2-217), all patients were alive and 3 had undergone cardiac transplantation, all of whom had CMR studies positive for myocarditis. Conclusions This is the largest study to date describing the CMR findings in children with myocarditis. The CMR techni- ques used, from most to least common, were LGE, T2W, FPP, and EGE. Abnormalities were most often seen with LGE followed by T2W, EGE, and FPP. There was significant practice variation in the CMR protocol between institutions. The information from this study should be useful in planning a prospective study to eval- uate the diagnostic and predictive performance of CMR in children with suspected myocarditis. Funding None.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.000 |
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.
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".