Alveolar dynamics during mechanical ventilation in the healthy and injured lung
Bibliographic record
Abstract
Mechanical ventilation is a life-saving therapy in patients with acute respiratory distress syndrome (ARDS). However, mechanical ventilation itself causes severe co-morbidities in that it can trigger ventilator-associated lung injury (VALI) in humans or ventilator-induced lung injury (VILI) in experimental animal models. Therefore, optimization of ventilation strategies is paramount for the effective therapy of critical care patients. A major problem in the stratification of critical care patients for personalized ventilation settings, but even more so for our overall understanding of VILI, lies in our limited insight into the effects of mechanical ventilation at the actual site of injury, i.e., the alveolar unit. Unfortunately, global lung mechanics provide for a poor surrogate of alveolar dynamics and methods for the in-depth analysis of alveolar dynamics on the level of individual alveoli are sparse and afflicted by important limitations. With alveolar dynamics in the intact lung remaining largely a "black box," our insight into the mechanisms of VALI and VILI and the effectiveness of optimized ventilation strategies is confined to indirect parameters and endpoints of lung injury and mortality.In the present review, we discuss emerging concepts of alveolar dynamics including alveolar expansion/contraction, stability/instability, and opening/collapse. Many of these concepts remain still controversial, in part due to limitations of the different methodologies applied. We therefore preface our review with an overview of existing technologies and approaches for the analysis of alveolar dynamics, highlighting their individual strengths and limitations which may provide for a better appreciation of the sometimes diverging findings and interpretations. Joint efforts combining key technologies in identical models to overcome the limitations inherent to individual methodologies are needed not only to provide conclusive insights into lung physiology and alveolar dynamics, but ultimately to guide critical care patient therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".