Lung-protective ventilation in the management of congenital diaphragmatic hernia
Bibliographic record
Abstract
Prioritizing lung-protective ventilation has produced a clear mortality benefit in neonates with congenital diaphragmatic hernia (CDH). While there is a paucity of CDH-specific evidence to support any particular approach to lung-protective ventilation, a growing body of data in adults is beginning to clarify the mechanisms behind ventilator-induced lung injury and inform safer management of mechanical ventilation in general. This review summarizes the adult data and attempts to relate the findings, conceptually, to the CDH population. Critical lessons from the adult studies are that much of the damage done during conventional mechanical ventilation affects normal lung tissue and that most of this damage occurs at the low-volume and high-volume extremes of the respiratory cycle. Consequently, it is important to prevent atelectasis by using sufficient positive end-expiratory pressure while also avoiding overdistention by scaling tidal volume to the amount of functional lung tissue rather than body weight. Paralysis early in acute respiratory distress syndrome improves outcomes, possibly because consistent respiratory mechanics facilitate avoidance of both atelectasis and overdistention-a mechanism that may also apply to the CDH population. Volume-targeted conventional modes may be advantageous in CDH, but determining optimal tidal volume is challenging. Both high-frequency oscillatory ventilation and high-frequency jet ventilation have been used successfully as 'rescue modes' to avoid extracorporeal membrane oxygenation, and a prospective trial comparing the two high-frequency modalities as the primary ventilation strategy for CDH is underway.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".