Application of Neonatologist Performed Echocardiography in the Assessment and Management of Neonatal Heart Failure unrelated to Congenital Heart Disease
Bibliographic record
Abstract
Neonatal heart failure (HF) is a progressive disease caused by cardiovascular and non-cardiovascular abnormalities. The most common cause of neonatal HF is structural congenital heart disease, while neonatal cardiomyopathy represents the most common cause of HF in infants with a structurally normal heart. Neonatal cardiomyopathy is a group of diseases manifesting with various morphological and functional phenotypes that affect the heart muscle and alter cardiac performance at, or soon after birth. The clinical presentation of neonates with cardiomyopathy is varied, as are the possible causes of the condition and the severity of disease presentation. Echocardiography is the selected method of choice for diagnostic evaluation, follow-up and analysis of treatment results for cardiomyopathies in neonates. Advances in neonatal echocardiography now permit a more comprehensive assessment of cardiac performance that could not be previously achieved with conventional imaging. In this review, we discuss the current and emerging echocardiographic techniques that aid in the correct diagnostic and pathophysiological assessment of some of the most common etiologies of HF that occur in neonates with a structurally normal heart and acquired cardiomyopathy and we provide recommendations for using these techniques to optimize the management of neonate with 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".