Pharmacology in Congenital Diaphragmatic Hernia: A Focus on Cardiovascular Management
Bibliographic record
Abstract
Congenital diaphragmatic hernia (CDH) presents a complex challenge in neonatal care, requiring tailored pharmacological strategies to manage its distinct cardiorespiratory pathophysiology. CDH is commonly associated with pulmonary hypertension, impaired myocardial function, and adverse cardiorespiratory interactions, contributing to significant morbidity and mortality. Effective pharmacotherapy must address these interconnected factors while minimizing complications or side effects. Despite limited randomized controlled trial data specific to CDH, recent reports highlight the benefits of a precision medicine approach, focusing on individualized treatments based on evolving pathophysiology. Therapeutic interventions primarily involve pulmonary vasodilators, inotropes and vasopressors, prostaglandins, and corticosteroids; each agent has a distinct physiologic effect, and use needs to be tailored to the specific patient pathophysiology. Targeted neonatal echocardiography has emerged as a valuable tool for optimizing treatment decisions by providing real-time insights into ventricular performance and hemodynamic status. In this review, we explore the shift from a generalized pharmacological approach to targeted interventions based on evolving patient physiology. We discuss key therapeutic principles and the role of different drug classes in optimizing the management of infants with CDH throughout their intensive care journey.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".