Correlation of late gadolinium enhancement MRI and quantitative T2 measurement in cardiac sarcoidosis
Bibliographic record
Abstract
PURPOSE: To investigate the potentially improved detection and quantification of cardiac involvement using novel late-gadolinium-enhancement (LGE) cardiac magnetic resonance imaging (MRI) and quantitative T2 measurement to achieve better myocardial tissue characterization in systemic sarcoidosis. MATERIALS AND METHODS: Twenty-eight patients with systemic sarcoidosis underwent a cardiac magnetic resonance imaging (CMR) study on a 1.5T system. Precontrast CMR included left ventricular (LV) and right ventricular (RV) function and quantitative T2 measurement. Postcontrast LGE-MRI included inversion-recovery fast-gradient-echo (IR-FGRE) and multicontrast late-enhancement imaging (MCLE). RESULTS: LV functional parameters were normal in all patients (LVEF=61.2±8.5%) including with cardiac involvement (LVEF=59.4±12.1%) and without (LVEF=61.7±7.5%) while the average RV function was comparatively decreased (RVEF=48.0±6.6%, P<0.0001). 21.4% of patients had cardiac involvement showing patchy or multiple focal hyperenhancement patterns in LV free wall, papillary muscles (PM), or interventricular septum. In two cases with PM involvement, the PM abnormal LGE foci were only observed on MCLE. For precontrast T2 measurements, a significantly decreased T2 measurement was observed in regions demonstrating LGE, compared to the LGE-negative group (focal LGE-positive regions vs. negative: 40.0±2.4 msec vs. 53.0±2.6 msec, P<0.0001). CONCLUSION: LGE-MRI can identify cardiac involvement in systemic sarcoidosis. MCLE might be more sensitive at detecting subtle myocardial lesion. The decreased T2 observed in cardiac sarcoid may reflect its inactive phase, thus might provide a noninvasive method for monitoring disease activity or therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".