Effect of tidal volume and positive end-expiratory pressure on expiratory time constants in experimental lung injury
Bibliographic record
Abstract
We utilized a multicompartment model to describe the effects of changes in tidal volume (VT) and positive end-expiratory pressure (PEEP) on lung emptying during passive deflation before and after experimental lung injury. Expiratory time constants (τE) were determined by partitioning the expiratory flow-volume (V˙EV) curve into multiple discrete segments and individually calculating τE for each segment. Under all conditions of PEEP and VT, τE increased throughout expiration both before and after injury. Segmented τE values increased throughout expiration with a slope that was different than zero (P < 0. 01). On average, τE increased by 45.08 msec per segment. When an interaction between injury status and τE segment was included in the model, it was significant (P < 0.05), indicating that later segments had higher τE values post injury than early τE segments. Higher PEEP and VT values were associated with higher τE values. No evidence was found for an interaction between injury status and VT, or PEEP. The current experiment confirms previous observations that τE values are smaller in subjects with injured lungs when compared to controls. We are the first to demonstrate changes in the pattern of τE before and after injury when examined with a multiple compartment model. Finally, increases in PEEP or VT increased τE throughout expiration, but did not appear to have effects that differed between the uninjured and injured state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".