P1075Benefit of cardiac magnetic resonance imaging tissue characterization in risk profiling patients with suspected myocarditis
Bibliographic record
Abstract
Background: Diagnosis and prognostication of patients with possible myocarditis are challenging given the non-specificity of clinical signs and symptoms. Cardiac magnetic resonance imaging (CMR) has become a key investigative tool in such presentations, but the prognostic implications of CMR remains unclear. Objectives: Event-free survival analyses of a large patient cohort who presented with suspected myocarditis who underwent CMR. Methods: 670 Patients with clinically suspected myocarditis were included. CMR findings including the presence and extent of late gadolinium enhancement (LGE) were associated with major adverse cardiac events (MACE), including all-cause death, heart failure, heart transplantation, documented sustained ventricular arrhythmia, or recurrent myocarditis. Results: At a median follow-up of 4.7 [interquartile-range (IQR) 2.3–7.3] years, 98 patients experienced a MACE. 294 (44%) patients presented with LGE on CMR. LGE presence was associated with a more than 2-fold increase in risk of MACE (HR 2.22, 95% CI: 1.47–3.35; p<0.001). Each percentage increase in LGE extent was associated with a 6% increase in risk of MACE (HR 1.06, 95% CI: 1.02–1.06, p=0.001). Annualized event rates of MACE with regards to presence or absence of LGE were 4.8% versus 2.1% (p<0.001). Midwall LGE involvement was associated with a HR of 2.39 (95% CI: 1.54–3.69; p<0.001) with MACE, but not epicardial LGE involvement (p=0.291). Strongest association was observed in septal LGE involvement (HR 2.55, 95% CI: 1.77–3.83; p<0.001), whereas lateral involvement was not associated with MACE (p=0.145).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".