Cardiovascular magnetic resonance left ventricular 4D-flow: differences in flow components and kinetic energy across heart failure spectrum
Bibliographic record
Abstract
Abstract Aims Cardiovascular magnetic resonance (CMR) 4-dimensional (4D) intraventricular flow analysis quantifies volume and kinetic energy (KE) of direct flow (DF), and residual volume (ReV) components, illustrating heart failure (HF) haemodynamic changes. Study aims were (1) compare volume and KE indexed (KEi) of DF and ReV between groups. (2) Assess relationship between 4D-flow parameters with CMR parameters. Methods and results 187 subjects (63.0 ± 17.1 years; 101 males) comprising 78 controls, 47 HF with preserved ejection fraction (HFpEF), 25 HF with mildly reduced ejection fraction (HFmrEF), 37 HF with reduced ejection fraction (HFrEF) were included. Volume and KEi of DF, and ReV were obtained from 4D flow CMR images. Controls had highest DF volume and systolic KEi (control 35.0% and 54.7 µJ/mL), followed by HFpEF (22.7% and 61.4 µJ/mL), HFmrEF (13.1% and 43.3 µJ/mL), HFrEF (5.2% and 33.1 µJ/mL) (P < 0.001). ReV and diastolic KEi were lowest in controls (26.0% and 7.9 µJ/mL), and higher across HFpEF (31.8% and 7.8 µJ/mL), HFmrEF (41.6% and 10.8 µJ/mL), HFrEF (49.5% and 11.5 µJ/mL) (P < 0.001). DF volume correlated positively with left ventricular ejection fraction (LVEF) (r = 0.794), but negatively with LV-end-diastolic volume indexed (EDVi) (r = −0.563) (all P < 0.001). ReV correlated negatively with LVEF (r = −0.737) but positively with LV-EDVi (r = 0.602) (all P < 0.001). Loss of two diastolic peaks in KE time curves for HF patients were shown. Conclusion CMR 4D DF and ReV with their KEi showed haemodynamic changes and KEi time curve pattern distortions in HF.
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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 | 0.001 |
| 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) | 0.002 | 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".