4D-Flow MRI Characterization of Pulmonary Flow in Repaired Tetralogy of Fallot
Bibliographic record
Abstract
Patients with Tetralogy of Fallot (TOF) have multiple surgical sequelae altering the pulmonary flow hemodynamics. Repaired TOF (rTOF) adults frequently develop pulmonary regurgitation impacting the blood flow pressure, right ventricle load, and pulmonary hemodynamics. We aimed to evaluate the pulmonary flow hemodynamics using 4D-flow magnetic resonance imaging (MRI) for characterizing altered blood flow, viscous energy loss (EL), wall shear stress (WSS), pressure drop (PD), and ventricular flow analysis (VFA) in rTOF patients. We hypothesized that 4D-flow based parameters can identify pulmonary blood flow alterations. A total of 17 rTOF patients (age: 29 ± 10 years, 35% women) and 20 controls (age: 36 ± 12 years, 25% women) were scanned using a dedicated cardiac MRI protocol. Peak velocity and regurgitant fraction were significantly higher for rTOF patients (p < 0.001). WSS was consistently elevated along the PA in the rTOF (p ≤ 0.05). The rTOF average circumferential WSS was higher than axial WSS at the main pulmonary artery (p ≤ 0.001). PD and EL were consistently higher in the rTOF as compared with controls (p < 0.05). For VFA, delayed ejection increased and retained inflow decreased in rTOF patients (p < 0.001). To conclude, this study demonstrated that 4D-flow MRI pulmonary flow in the rTOF can exhibit altered peak velocity, valvular regurgitation, WSS, EL, PD, and VFA.
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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) | 0.001 | 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".