Surgical management of the diaphragmatic defect in congenital diaphragmatic hernia: a contemporary review
Bibliographic record
Abstract
Worldwide, 150 children are born each day with congenital diaphragmatic hernia (CDH), a diaphragmatic defect with concomitant abnormal lung development. Patients with CDH with large defects are particularly challenging to treat, have the highest mortality, and are at significant risk of long-term complications. Advances in prenatal and neonatal treatments have improved survival in high-risk patients with CDH, but surgical treatment of large defects lacks standardization. Open repair by an abdominal approach has long been considered the traditional procedure, but the type of defect repair (patch or muscle flap) and patch material (non-absorbable, synthetic or absorbable, biological) remain subjects of debate. Increased experience and improved techniques in minimally invasive surgery (MIS) have expanded selection criteria for thoracoscopic defect repair in cardiopulmonary stable patients with small defects. However, the application of MIS to repair large defects remains controversial due to increased recurrence rates and unknown long-term effects of perioperative hypercapnia and acidosis resulting from capnothorax and reduced ventilation. Current recommendations on the surgical management rely on cohort studies of varying patient numbers and data on the long-term outcomes are sparse. Here, we discuss surgical approaches for diaphragmatic defect repair highlighting advancements, and knowledge gaps in surgical techniques (open surgery and MIS), patch materials and muscle flaps for large defects, as well as procedural adjuncts and management of CDH variants.
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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 | 0.004 | 0.004 |
| 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) | 0.003 | 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".