Determining Early Remodeling Patterns in Diabetes and Hypertension Using Cardiac Computed Tomography: The Feasibility of Assessing Early LV Geometric Changes
Bibliographic record
Abstract
BACKGROUND: There is conflicting data on early left ventricle (LV) remodeling in diabetes mellitus (DM) and hypertension (HTN). This study examines the feasibility of cardiac computed tomography angiography (CCTA) to detect early LV geometric changes in patients with DM and HTN. METHODS: Consecutive patients (n = 5,992) who underwent prospective electrocardiography (ECG)-triggered (mid-diastolic) CCTA were screened. Patients with known structural heart disease or known LV dysfunction were excluded. Left ventricular mass (LVM), left ventricular mid-diastolic volume (LVMDV), and LV concentricity (LVM/LVMDV) were measured and indexed to body surface area. RESULTS: A total of 4,283 patients were analyzed (mean age 57 ± 10.69 years, female 46.7%). DM, HTN, and HTN + DM were present in 4.1%, 35.8% and 10.6% of patients, respectively. Compared to normal patients, HTN and HTN + DM patients had increased LVM indexed to body surface area (LVMi) (56.87 ± 17.24, 59.26 ± 13.62, and 58.56 ± 13.09, respectively; P < 0.05). There was no difference in LVMi between normal subjects and patients with DM (56.39 ± 11.50, P = 0.617).Concentricity indices were higher in patient with HTN (1.0456 ± 0.417; P < 0.001), DM (1.109 ± 0.638; P = 0.004), and HTN + DM (1.083 ± 0.311, P < 0.001) than normal individuals (0.9671 ± 0.361). There was no overlap of the 95% confidence intervals in the composite of concentricity indices and LVMi between the different groups. CONCLUSIONS: CCTA measures of LVM and concentricity index may discriminate patients with HTN and DM before overt structural heart disease.
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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 | 0.002 |
| 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 | 0.000 |
| 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".