Understanding ventilator-induced lung injury: The role of mechanical power
Bibliographic record
Abstract
Mechanical ventilation stands as a life-saving intervention in the management of respiratory failure. However, it carries the risk of ventilator-induced lung injury. Despite the adoption of lung-protective ventilation strategies, including lower tidal volumes and pressure limitations, mortality rates remain high, leaving room for innovative approaches. The concept of mechanical power has emerged as a comprehensive metric encompassing key ventilator parameters associated with the genesis of ventilator-induced lung injury, including volume, pressure, flow, resistance, and respiratory rate. While numerous animal and human studies have linked mechanical power and ventilator-induced lung injury, its practical implementation at the bedside is hindered by calculation challenges, lack of equation consensus, and the absence of an optimal threshold. To overcome the constraints of measuring static respiratory parameters, dynamic mechanical power is proposed for all patients, regardless of their ventilation mode. However, establishing a causal relationship is crucial for its potential implementation, and requires further research. The objective of this review is to explore the role of mechanical power in ventilator-induced lung injury, its association with patient outcomes, and the challenges and potential benefits of implementing a ventilation strategy based on mechanical power.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".