Role of Positive End-Expiratory Pressure and Regional Transpulmonary Pressure in Asymmetrical Lung Injury
Bibliographic record
Abstract
Abstract Rationale Asymmetrical lung injury is a frequent clinical presentation. Regional distribution of Vt and positive end-expiratory pressure (PEEP) could result in hyperinflation of the less-injured lung. The validity of esophageal pressure (Pes) is unknown. Objectives To compare, in asymmetrical lung injury, Pes with directly measured pleural pressures (Ppl) of both sides and investigate how PEEP impacts ventilation distribution and the regional driving transpulmonary pressure (inspiratory – expiratory). Methods Fourteen mechanically ventilated pigs with lung injury were studied. One lung was blocked while the contralateral one underwent surfactant lavage and injurious ventilation. Airway pressure and Pes were measured, as was Ppl in the dorsal and ventral pleural space adjacent to each lung. Distribution of ventilation was assessed by electrical impedance tomography. PEEP was studied through decremental steps. Measurements and Results Ventral and dorsal Ppl were similar between the injured and the noninjured lung across all PEEP levels. Dorsal Ppl and Pes were similar. The driving transpulmonary pressure was similar in the two lungs. Vt distribution between lungs was different at zero end-expiratory pressure (≈70% of Vt going in noninjured lung) owing to different respiratory system compliance (8.3 ml/cm H2O noninjured lung vs. 3.7 ml/cm H2O injured lung). PEEP at 10 cm H2O with transpulmonary pressure around zero homogenized Vt distribution opening the lungs. PEEP ≥16 cm H2O equalized distribution of Vt but with overdistension for both lungs. Conclusions Despite asymmetrical lung injury, Ppl between injured and noninjured lungs is equalized and esophageal pressure is a reliable estimate of dorsal Ppl. Driving transpulmonary pressure is similar for both lungs. Vt distribution results from regional respiratory system compliance. Moderate PEEP homogenizes Vt distribution between lungs without generating hyperinflation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".