Recent advances in multimodal imaging in tetralogy of fallot and double outlet right ventricle
Bibliographic record
Abstract
PURPOSE OF REVIEW: In the ever-evolving field of medical imaging, this review highlights significant advancements in preoperative and postoperative imaging for Tetralogy of Fallot (TOF) and double outlet right ventricle (DORV) over the past 18 months. RECENT FINDINGS: This review showcases innovations in echocardiography such as 3D speckle tracking echocardiography (3DSTE) for assessing right ventricle-pulmonary artery coupling (RVPAC) and Doppler velocity reconstruction (DoVeR) for intracardiac flow fields evaluation. Furthermore, advances in assessment of cardiovascular anatomy using computed tomography (CT) improve the integration of imaging in ablation procedures. Additionally, the inclusion of cardiac magnetic resonance (CMR) parameters as risk score predictors for morbidity, and mortality and for timing of pulmonary valve replacement (PVR) indicates its significance in clinical management. The utilization of 4D flow techniques for postoperative hemodynamic assessment promises new insights into pressure mapping. Lastly, emerging technologies such as 3D printing and 3D virtual reality are expected to improve image quality and surgical confidence in preoperative planning. SUMMARY: Developments in multimodality imaging in TOF and DORV are poised to shape the future of clinical practice in this field.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".