Cardiovascular magnetic resonance based diagnosis of left ventricular non-compaction cardiomyopathy: impact of cine bSSFP strain analysis
Bibliographic record
Abstract
BACKGROUND: Investigation of the myocardial strain characteristics of the left ventricular non-compaction (LVNC) phenotype with cardiovascular magnetic resonance (CMR) feature tracking. METHODS: CMR cine balanced steady-state free precession data sets of 59 retrospectively identified LVNC phenotype patients (40 years, IQR: 28-50 years; 51% male) and 36 healthy subjects (39 years, IQR: 30-47 years; 44% male) were evaluated for LV volumes, systolic function and mass. Hypertrabeculation in patients and healthy subjects was evaluated against established CMR diagnostic criteria. Global circumferential strain (GCS), global radial strain (GRS) and global longitudinal strain (GLS) were evaluated with feature-tracking software. Subgroup analyses were performed in patients (n = 25) and healthy subjects (n = 34) with normal LV volumetrics, and with healthy subjects (n = 18) meeting at least one LVNC diagnostic criteria. RESULTS: (100% vs. 41.7%). LVNC phenotype patients demonstrated reduced GRS (26.4% vs. 37.1%; p < 0.001), GCS (- 16.5% vs. -20.5%; p < 0.001) and GLS (- 14.6% vs. -17.1%; p < 0.001) compared to healthy subjects, with statistically significant differences persisting on subgroup comparisons of LVNC phenotype patients with healthy subjects meeting diagnostic criteria. GCS also demonstrated independent and incremental diagnostic value beyond each of the morphology-based CMR diagnostic criteria. CONCLUSIONS: LVNC phenotype patients demonstrate impaired strain by CMR feature tracking, also present on comparison of subjects with normal LV volumetrics meeting diagnostic criteria. The high proportion of healthy subjects meeting morphology-based CMR diagnostic criteria emphasizes the important potential complementary diagnostic value of strain in differentiating LVNC from physiologic hypertrabeculation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".