Lung recruitment in neonatal high‐frequency oscillatory ventilation with volume‐guarantee
Bibliographic record
Abstract
Abstract Background and Objectives The optimal lung volume strategy during high‐frequency oscillatory ventilation (HFOV) is reached by performing recruitment maneuvers, usually guided by the response in oxygenation. In animal models, secondary spontaneous change in oscillation pressure amplitude (ΔPhf) associated with a progressive increase in mean airway pressure during HFOV combined with volume guarantee (HFOV‐VG) identifies optimal lung recruitment. The aim of this study was to describe recruitment maneuvers in HFOV‐VG and analyze whether changes in ΔPhf might be an early predictor for lung recruitment in newborn infants with severe respiratory failure. Design and Methods The prospective observational study was done in a tertiary‐level neonatology department. Changes in ΔPhf were analyzed during standardized lung recruitment after initiating early rescue HFOV‐VG in preterm infants with severe respiratory failure. Results Twenty‐seven patients were included, with a median gestational age of 24 weeks (interquartile range [IQR]: 23–25). Recruitment maneuvers were performed, median baseline mean airway pressure (mPaw) was 11 cm H2O (IQR: 10–13), median critical lung opening mPaw during recruitment was 14 cm H2O (IRQ: 12–16), and median optimal mPaw was 12 cm H2O (IQR: 10–14, p < 0.01). Recruitment maneuvers were associated with an improvement in oxygenation (FiO2: 65.0 vs. 45.0, p < 0.01, SpO2/FiO2 ratio: 117 vs. 217, p < 0.01). ΔPhf decreased significantly after lung recruitment (mean amplitude: 23.0 vs. 16.0, p < 0.01). Conclusion In preterm infants with severe respiratory failure, the lung recruitment process can be effectively guided by ΔPhf on HFOV‐VG.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".