Intra-Cardiac Kinetic Energy and Ventricular Flow Analysis in Bicuspid Aortic Valve: Impact on Left Ventricular Function, Dilation Severity, and Surgical Referral
Bibliographic record
Abstract
Intra-cardiac kinetic energy (KE) and ventricular flow analysis (VFA), as derived from 4D-flow MRI, can be used to understand the physiological burden placed on the left ventricle (LV) due to bicuspid aortic valve (BAV). Our hypothesis was that the KE of each VFA component would impact the surgical referral outcome depending on LV function decrement, BAV phenotype, and aortic dilation severity. A total of 11 healthy controls and 49 BAV patients were recruited. All subjects underwent cardiac magnetic resonance imaging (MRI) examination. The LV mass was inferior in the controls than in the BAV patients (90 ± 26 g vs. 45 ± 17 g, p = 0.025), as well as the inferior ascending aorta diameter indexed (15.8 ± 2.5 mm/m2 vs. 19.3 ± 3.5 mm/m2, p = 0.005). The VFA KE was higher in the BAV group; significant increments were found for the maximum KE and mean KE in the VFA components (p < 0.05). A total of 14 BAV subjects underwent surgery after the scans. When comparing BAV nonsurgery vs. surgery-referred cohorts, the maximum KE and mean KE were elevated (p < 0.05). The maximum and mean KE were also associated with surgical referral (r = 0.438, p = 0.002 and r = 0.371, p = 0.009, respectively). In conclusion, the KE from VFA components significantly increased in BAV patients, including in BAV patients undergoing surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".