Cardiac CT assessment of left ventricular mass in mid-diastasis and its prognostic value
Bibliographic record
Abstract
Aims To determine the influence of cardiac motion on measurements of left ventricular (LV) mass obtained with 64-slice computed tomography (CT) and to elucidate the prognostic value of LV mass on major adverse cardiac events (MACE) and all-cause mortality. Increased LV mass has been linked with MACE. Although Cardiac CT allows measurement of LV anatomy, it is susceptible to motion artefacts often requiring image acquisition during diastasis. There is a need to understand variability in LV mass measurements across phases of the cardiac cycle, and whether mid-diastolic measurements have prognostic value. Methods and results The study comprised two equally sized cohorts of patients that had undergone retrospectively gated cardiac CT: patients who had MACE and/or all-cause death at follow-up and a matched (age, sex, and risk factors) event-free cohort. LV mass was measured at mid-diastole, end-diastole, and end-systole. Correlation and agreement between phases were determined. The incremental value of mid-diastolic hypertrophy (LVH) over the National Cholesterol Education Programme (NCEP) risk was performed for LV mass indices normalized to body surface area (LVMIBSA) or weight (LVMIWeight). Of 166 patients, 31.3% experienced MACE and 28.9% died of any cause (follow-up 22.9 ± 13.4 months). LV mass at all cardiac phases were strongly correlated (r > 0.94). Mean mid-diastolic LVMIBSA was higher in the cohort with events (93.7 vs. 80.7 g/m2, P= 0.008) as was LVMIWeight (2.26 vs. 1.88 g/kg, P = 0.001). LVMIBSA and LVMIWeight had prognostic value incremental to NCEP with 1.85 and 2.47 hazard ratios, respectively. Conclusions Measurement of LV mass can be obtained by cardiac CT images obtained at mid-diastasis. LV mass measurements obtained at mid-diastasis have prognostic value.
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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".