Veterinary and technical optimization of the fetal sheep model of congenital diaphragmatic hernia: implications for translational pediatric surgery
Bibliographic record
Abstract
Congenital diaphragmatic hernia (CDH) is a life-threatening developmental anomaly where abdominal organs herniate into the thoracic cavity, impairing fetal lung growth and subsequent postnatal lung function. Despite advances in treatment, the morbidity and mortality of CDH remain significant. Currently, the most well-established fetal intervention is fetoscopic endoluminal tracheal occlusion (FETO), which promotes lung expansion and development by temporarily blocking the egress of lung fluid. However, treatment outcomes remain variable, which underscores the need for robust animal models to investigate novel therapies. The fetal sheep model is particularly valuable due to physiological similarities to human infants in lung development and anatomy. However, its successful implementation requires substantial veterinary and surgical expertise. In this paper, we outline the surgical protocol, refinements, and perioperative challenges in establishing a fetal sheep model of CDH to test a novel therapy. A diaphragmatic defect was surgically created via fetal thoracotomy at 80 days of gestation using a maternal caudal ventral midline laparotomy. Fetal tracheal occlusion with treatment administration was performed via a maternal left flank laparotomy at 108 days, followed by euthanasia then delivery at 136 days. Initial surgeries experienced complications such as maternal incisional dehiscence and herniation. These were mitigated through changes in surgical approach, closure techniques, and enhanced postoperative care. Veterinary oversight was critical in optimizing maternal well-being, minimizing stress, and improving recovery outcomes. This refined model provides a reproducible, welfare-centred approach integrating essential veterinary contributions to support translational pediatric surgery research in CDH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".