MR Imaging of Arrhythmogenic Right Ventricular Cardiomyopathy: Morphologic Findings and Interobserver Reliability
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance (MR) imaging is frequently used to diagnose arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D). However, the reliability of various MR imaging features for diagnosing ARVC/D is unknown. The purpose of this study was to determine which morphologic MR imaging features have the greatest interobserver reliability for diagnosing ARVC/D. METHODS: Forty-five sets of films of cardiac MR images were sent to 8 radiologists and 5 cardiologists with experience in this field. There were 7 cases of definite ARVC/D as defined by the Task Force criteria. Six cases were controls. The remaining 32 cases had MR imaging because of clinical suspicion of ARVC/D. Readers evaluated the images for the presence of (a) right ventricle (RV) enlargement, (b) RV abnormal morphology, (c) left ventricle enlargement, (d) presence of high T(1) signal (fat) in the myocardium, and (e) location of high T(1) signal (fat) on a Likert scale with formatted responses. RESULTS: Readers indicated that the Task Force ARVC/D cases had significantly more (chi(2) = 119.93, d.f. = 10, p < 0.0001) RV chamber size enlargement (58%) than either the suspected ARVC/D (12%) or no ARVC/D (14%) cases. When readers reported the RV chamber size as enlarged they were significantly more likely to report the case as ARVC/D present (chi(2)(= )33.98, d.f. = 1, p < 0.0001). When readers reported the morphology as abnormal they were more likely to diagnose the case as ARVC/D present (chi(2) = 78.4, d.f. = 1, p < 0.0001), and the Task Force ARVC/D (47%) cases received significantly more abnormal reports than either suspected ARVC/D (20%) or non-ARVC/D (15%) cases. There was no significant difference between patient groups in the reported presence of high signal intensity (fat) in the RV (chi(2) = 0.9, d.f. = 2, p > 0.05). CONCLUSIONS: Reviewers found that the size and shape of abnormalities in the RV are key MR imaging discriminates of ARVD. Subsequent protocol development and multicenter trials need to address these parameters. Essential steps in improving accuracy and reducing variability include a standardized acquisition protocol and standardized analysis with dynamic cine review of regional RV function and quantification of RV and left ventricle volumes.
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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 | 0.021 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".