Validation of sub‐segmental visual scoring for the quantification of ischemic and nonischemic myocardial fibrosis using late gadolinium enhancement MRI
Bibliographic record
Abstract
PURPOSE: To determine the accuracy and reproducibility of late gadolinium enhancement (LGE) MRI scar quantification using visual sub-segmental analysis (VSSA) versus signal threshold-based analysis in ischemic and nonischemic cardiomyopathy. MATERIALS AND METHODS: One-hundred sixty-one patients with abnormal LGE imaging underwent VSSA and signal threshold-based analysis. VSSA was performed using a 68 sub-segmental model. Signal threshold-based analysis was performed using cutoffs of ≥2, ≥3, and ≥5 standard deviations (SD) above the mean signal of normal myocardium. Comparison of VSSA and signal threshold-based analysis was performed by linear regression and Bland Altman plots. RESULTS: Seventy (44%) patients had ischemic scar, 76 (47%) had nonischemic scar, and 15 (9%) had a combined pattern. Correlation coefficients for VSSA versus signal threshold-based analysis at ≥2, ≥3, and ≥5SD thresholds were r = 0.63, r = 0.79, r = 0.81 (P < 0.001) for all patients, r = 0.74, r = 0.81, r = 0.81 (P < 0.001) in those with ischemic scar, and r = 0.46, r = 0.69, r = 0.72 (P < 0.001) in those with nonischemic scar. Bland Altman analysis revealed no significant bias in total scar volume among all patients (-4.3 ± 7.9%), those with ischemic scar (-4.8 ± 7.8%), or those with nonischemic scar (-2.6 ± 7.6%). Intra-observer and inter-observer variability of the VSSA technique was excellent with a mean difference in total percent scar of 0.3% (-8.3-8.9%) and -0.4% (-9.5-8.5%), respectively. CONCLUSION: A VSSA-based model of myocardial scar quantification is accurate and reproducible in ischemic and nonischemic cardiomyopathy.
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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 | 0.001 | 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".