Congenital Diaphragmatic Hernia: State of the Art in Translating Experimental Research to the Bedside
Bibliographic record
Abstract
Congenital diaphragmatic hernia (CDH) is a devastating disease that still carries a high mortality and morbidity rate. Poor outcomes for fetuses and infants with CDH are mainly related to pulmonary hypoplasia (PH) and pulmonary vascular remodeling that leads to pulmonary hypertension (PHTN). Over the last five decades, research efforts have focused on modeling CDH not only to study the pathophysiology of the diaphragmatic defect, pulmonary hypoplasia, and pulmonary hypertension, but also to identify therapies that would promote lung growth and maturation, and correct vascular remodeling. As CDH is a multifactorial condition whose etiology remains unknown, there is not a single model of CDH, rather several ones that replicate different aspects of this disease. While small animals like the mouse and the rat have mainly been used to uncover biological pathways underlying the diaphragmatic defect and poor lung growth, larger animals like the lamb and the rabbit models have been instrumental for pursuing medical and surgical interventions. Overall, the use of animal models has indeed advanced our knowledge on CDH and helped us test innovative therapeutic options. For example, the lamb model of CDH has been the paradigm for testing fetal surgical procedures, including tracheal occlusion, which has been translated to clinical use. In this review, we outline the induction protocols of CDH in animals with the use of chemicals, dietary changes, genetic alterations, and surgical maneuvers, and we describe the studies that have translated experimental results to the bedside.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".