Relationship between electrocardiographic findings and Cardiac Magnetic Resonance phenotypes in patients with Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
Background Q waves and negative T waves are common electrocardiographic (ECG) abnormalities in patients with Hypertrophic Cardiomyopathy (HCM). Several studies correlated ECG findings with presence and extent of fibrosis and hypertrophy; however, their significance remains incompletely clarified. Our study aimed to explain the mechanism behind Q and negative T waves by comparing their positions on a 12-lead ECG with phenotypes observed at Late Gadolinium Enhancement (LGE) Cardiac Magnetic Resonance (CMR). Methods 12-lead ECG and LGE-CMR were performed in 88 consecutive patients with HCM (42 SD 16 years, 65 males). Using Delta Thickness ratio (DT ratio), and “global” and “parietal” LGE at CMR, the extent and distribution of myocardial hypertrophy and fibrosis were studied in correlation with ECG abnormalities. Results Q waves in different leads were not associated with “parietal” LGE score. Lateral Q waves correlated with an increased DT ratio Inferior Septum/Lateral wall ( p = 0.01). A similar correlation between inferior Q waves and an increased DT Ratio Anterior wall/Inferior wall was of borderline statistical significance ( p = 0.06). As expected, ECG signs of LV hypertrophy related to a raised Left Ventricular Mass Index (LVMI) ( p < 0.0001) and mean wall thickness ( p = 0.01). Depolarization disturbances, including negative T waves in lateral ( p = 0.044) and anterior ( p = 0.031) leads correlated with “parietal” LGE scores while QT dispersion ( p = 0.0001) was associated with “global” LGE score. Conclusion In HCM patients, Q waves are generated by asymmetric hypertrophy rather than by myocardial fibrosis, while negative T waves result from local LGE distribution at CMR.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".