In silico numerical simulation of ventilator settings during high‐frequency ventilation in preterm infants
Bibliographic record
Abstract
OBJECTIVE: Despite the routine use of antenatal steroids, exogenous surfactants, and different noninvasive ventilation methods, many extremely low gestational age neonates, preterm, and term infants eventually require invasive ventilation. In addition to prematurity, mechanical ventilation itself can induce ventilator-induced lung injury leading to lifelong pulmonary sequelae. Besides conventional mechanical ventilation, high-frequency oscillatory ventilation (HFOV) with tidal volumes below dead space and high ventilation frequencies is used either as a primary or rescue therapy in severe neonatal respiratory failure. METHODS AND RESULTS: Applying a high-resolution computational lung modeling technique in a preterm infant, we studied three different high-frequency ventilation settings as well as conventional ventilation (CV) settings. Evaluating the computed oxygen delivery (OD) and lung mechanics (LM) we outline for the first time how changing ventilator settings from CV to HFOV lead to significant improvements in OD and LM. CONCLUSION: This personalized "digital twin" strategy advances our general knowledge of protective ventilation strategies in neonatal care and can support decisions on various modes of ventilatory therapy at high frequencies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".