Distribution of regional lung aeration and perfusion during conventional and noisy pressure support ventilation in experimental lung injury
Bibliographic record
Abstract
In acute lung injury (ALI), pressure support ventilation (PSV) may improve oxygenation compared with pressure-controlled ventilation (PCV), and benefit from random variation of pressure support (noisy PSV). We investigated the effects of PCV, PSV, and noisy PSV on gas exchange as well as the distribution of lung aeration and perfusion in 12 pigs with ALI induced by saline lung lavage in supine position. After injury, animals were mechanically ventilated with PCV, PSV, and noisy PSV for 1 h/mode in random sequence. The driving pressure was set to a mean tidal volume of 6 ml/kg and positive end-expiratory pressure to 8 cmH₂O in all modes. Functional variables were measured, and the distribution of lung aeration was determined by static and dynamic computed tomography (CT), whereas the distribution of pulmonary blood flow (PBF) was determined by intravenously administered fluorescent microspheres. PSV and noisy PSV improved oxygenation and reduced venous admixture compared with PCV. Mechanical ventilation with PSV and noisy PSV did not decrease nonaerated areas but led to a redistribution of PBF from dorsal to ventral lung regions and reduced tidal reaeration and hyperinflation compared with PCV. Noisy PSV further improved oxygenation and redistributed PBF from caudal to cranial lung regions compared with conventional PSV. We conclude that assisted ventilation with PSV and noisy PSV improves oxygenation compared with PCV through redistribution of PBF from dependent to nondependent zones without lung recruitment. Random variation of pressure support further redistributes PBF and improves oxygenation compared with conventional PSV.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".