Pathophysiology of ventilator-associated lung injury
Bibliographic record
Abstract
PURPOSE OF REVIEW: Mechanical ventilation is essential for the support of critically ill patients, but may aggravate lung damage, leading to ventilator-associated lung injury (VALI). VALI results from a succession of events beginning with mechanical alteration of lung parenchyma, because of disproportionate stress and strain. The resulting structural tension initiates a biological inflammatory cascade; however, tension can reach the limits of stress, triggering the destruction of structures. This article reviews and discusses the ongoing research into the mechanisms of VALI and their implications for the management of ventilated patients. RECENT FINDINGS: Several experimental and clinical studies have been performed to evaluate the contribution of pathogenic mechanical forces to organ and cellular deformation and the implications for guiding ventilator management in patients at risk for VALI. VALI may be attenuated by reducing tidal volume, but the key variable in determining pulmonary overdistension is transpulmonary pressure. Other parameters associated with the induction of VALI include positive end-expiratory pressure, inspiratory airflow and time, and respiratory frequency. SUMMARY: How ventilation strategy, specific mechanisms of mechanotransduction, and their individual threshold values impact on VALI remains to be elucidated. In addition, clinical studies are required to evaluate the usefulness of individualized ventilator strategies based on lung mechanics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".