Fractal analysis of left ventricular trabeculae in hypertensive patients with heart failure: a 3.0 T cardiac magnetic resonance study
Bibliographic record
Abstract
Background Endocardial trabecular hyperplasia due to hemodynamic stress reflects phenotypic variability in disease progression. Employing fractal analysis, this study quantified left ventricular (LV) myocardial trabecular complexity in hypertensive patients with and without heart failure (HF) to evaluate its diagnostic utility for HF. Methods This study retrospectively enrolled 146 hypertensive patients (77 with HF, 69 without), grouped into HTN-HF ( n = 77) and HTN non-HF ( n = 69); additionally, 34 healthy controls were recruited. Clinical data and cardiac MRI parameters were compared. Fractal dimension (FD) values were calculated on the LV short-axis cine images using fractal analysis. Logistic regression analysis was performed to determine predictors. Results Five fractal dimensions were derived: global FD, along with mean/maximal apical FD and mean/maximal basal FD. Compared with healthy controls, HF patients showed significantly elevated left ventricular fractal dimensions (all P < 0.001). Moreover, these fractal dimensions exhibited significant differences between the HTN-HF patients and HTN non-HF patients, except for maximal basal FD. The univariate logistic regression revealed that global FD, mean/maximal apical FD and mean basal FD emerged as significant independent predictors (OR: 1.170,1.121,1.070, and 1.088, P < 0.05). Furthermore, integration of fractal dimensions enhanced calibration and diagnostic accuracy of the model. (AUC: 0.877). Conclusions CMR fractal analysis provides a feasible technique for quantifying LV myocardial trabecular complexity in hypertensive heart failure patients. In conclusion, our study demonstrates the potential of fractal analysis to provide incremental diagnostic value for heart failure within the hypertensive population. Integration of FD into clinical diagnostic models may enhance diagnostic performance.
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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 | 0.001 |
| 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) | 0.000 | 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 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".