Cardiovascular MR Imaging in Neonates and Infants with Congenital Heart Disease
Bibliographic record
Abstract
Cardiovascular magnetic resonance (MR) imaging has become an important alternative to echocardiography and angiocardiography in the evaluation of patients with congenital heart disease (CHD). It is increasingly being used in neonates and infants for the initial investigation of CHD or as follow-up after surgery or catheter-guided intervention. Specific indications for cardiovascular MR imaging in neonates and infants include investigation of the thoracic vasculature, quantification of the ventricular volumes, and evaluation of primary cardiac tumors. To obtain good-quality MR images in neonates and infants, it is essential to adjust the technical parameters of the pulse sequences to the small size and fast heart rates of the patients. Various MR imaging techniques are available that are effective in demonstrating the complex morphologic features of the cardiovascular system and that provide additional functional and hemodynamic information. The information provided by cardiovascular MR imaging is useful for treatment planning and, in many cases, may obviate potentially harmful cardiac catheterization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".