Lung resistance and elastance are different in ex vivo sheep lungs ventilated by positive and negative pressures
Bibliographic record
Abstract
Lung resistance ( RL) and elastance ( EL) can be measured during positive or negative pressure ventilation. Whether the different modes of ventilation produce different RL and EL is still being debated. Although negative pressure ventilation (NPV) is more physiological, positive pressure ventilation (PPV) is more commonly used for treating respiratory failure. In the present study, we measured lung volume, airway diameter, and airway volume, as well as RL and EL with PPV and NPV in explanted sheep lungs. We found that lung volume under a static pressure, either positive or negative, was not different. However, RL and EL were significantly higher in NPV at high inflation pressures. Interestingly, diameters of smaller airways (diameters <3.5 mm) and total airway volume were significantly greater at high negative inflation pressures compared with those at high positive inflation pressures. This suggests that NPV is more effective in distending the peripheral airways, likely due to the fact that negative pressure is applied through the pleural membrane and reaches the central airways via the peripheral airways, whereas positive pressure is applied in the opposite direction. More distension of lung periphery could explain why RL is higher in NPV (vs. PPV), because the peripheral parenchyma is a major source of tissue resistance, which is a part of the RL that increases with pressure. This explanation is consistent with the finding that during high frequency ventilation (>1 Hz, where RL reflects airway resistance more than tissue resistance), the difference in RL between NPV and PPV disappeared.
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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 | 0.001 |
| 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 | 0.001 |
| 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".