The utility of cardiac MRI in diagnosis of infective endocarditis: preliminary results
Bibliographic record
Abstract
PURPOSE: We aimed to evaluate the utility of cardiac magnetic resonance imaging (MRI) for the diagnosis of infective endocarditis (IE). METHODS: Sixteen patients with a preliminary diagnosis of IE (10 women and six men; age range, 4-66 years) were referred for cardiac MRI. MRI sequences were as follows: echo-planar cine true fast imaging with steady-state precession (true-FISP), dark-blood fast spin echo T1-weighted imaging, T2-weighted imaging, dark-blood half-Fourier single shot turbo spin echo (HASTE), and early contrast-enhanced first-pass fast low-angle shot (FLASH). Delayed contrast-enhanced images were obtained using three-dimensional inversion recovery FLASH after 15±5 min. The MRI features were evaluated, including valvular pathologies on cine MRI and contrast enhancement on the walls of the cardiac chambers, major thoracic vasculature, and paravalvular tissue, attributable to endothelial extension of inflammation on contrast-enhanced images. RESULTS: Fourteen valvular vegetations were detected in eleven patients on cardiac MRI. It was not possible to depict valvular vegetations in five patients. Vegetations were detected on the aortic valve (n=7), mitral valve (n=3), tricuspid and pulmonary valves (n=1). Delayed contrast enhancement attributable to extension of inflammation was observed on the aortic wall and aortic root (n=11), paravalvular tissue (n=4), mitral valve (n=2), walls of the cardiac chambers (n=6), interventricular septum (n=3), and wall of the pulmonary artery and superior mesenteric artery (n=1). CONCLUSION: Valvular vegetation features of IE can be detected by MRI. Moreover, in the absence of vegetations, detection of delayed enhancement representing endothelial inflammation of the cardiovascular structures can contribute to the diagnosis and treatment planning of IE.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".