Stress Hypoperfusion and Tissue Injury in Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Ischemia and tissue injury are common in patients with hypertrophic cardiomyopathy. Cardiovascular magnetic resonance imaging offers combined evaluations of each phenomenon at sufficiently high resolution to examine transmural spatial distribution. In this prospective cohort study, we examine the spatial distribution of stress perfusion abnormalities and tissue injury in patients with hypertrophic cardiomyopathy. METHODS AND RESULTS: One hundred consecutive patients with hypertrophic cardiomyopathy underwent cardiovascular magnetic resonance imaging. Cine, stress perfusion, late gadolinium enhancement, and T2-weighted imaging techniques were used. Each was spatially coregistered according to predefined segmental and subsegmental models and was blindly analyzed for abnormalities using validated techniques. Spatial associations among stress perfusion, late gadolinium enhancement, and T2 imaging were made at segmental and subsegmental levels. Of the 100 patients studied, the phenotype was septal in 86 and apical in 14. Late gadolinium enhancement imaging was abnormal in 79 patients (79%). Eighty-six patients met prespecified safety criteria to undergo stress perfusion, and ischemia was identified in 46 patients (57%). T2 imaging was available in 81 patients and was abnormal in 19 (29%). The dominant distribution of all 3 findings was to segment with hypertrophy. Subsegmental analysis revealed geographic dominance of ischemia within the subendocardial zones. However, this zone was most commonly spared from late gadolinium enhancement and T2 abnormalities, typically seen in midwall and subepicardial zones. CONCLUSIONS: Inducible hypoperfusion is a common finding in hypertrophic cardiomyopathy and is typically identified within segments exhibiting imaging markers of tissue injury. However, the respective transmural dominance of these phenomena seems distinct. Alternate factors contributing to a regional susceptibility to tissue injury are deserving of further study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".