Lung- and Diaphragm-Protective Ventilation
Bibliographic record
Abstract
Mechanical ventilation can cause acute diaphragm atrophy and injury, and this is associated with poor clinical outcomes. Although the importance and impact of lung-protective ventilation is widely appreciated and well established, the concept of diaphragm-protective ventilation has recently emerged as a potential complementary therapeutic strategy. This Perspective, developed from discussions at a meeting of international experts convened by PLUG (the Pleural Pressure Working Group) of the European Society of Intensive Care Medicine, outlines a conceptual framework for an integrated lung- and diaphragm-protective approach to mechanical ventilation on the basis of growing evidence about mechanisms of injury. We propose targets for diaphragm protection based on respiratory effort and patient-ventilator synchrony. The potential for conflict between diaphragm protection and lung protection under certain conditions is discussed; we emphasize that when conflicts arise, lung protection must be prioritized over diaphragm protection. Monitoring respiratory effort is essential to concomitantly protect both the diaphragm and the lung during mechanical ventilation. To implement lung- and diaphragm-protective ventilation, new approaches to monitoring, to setting the ventilator, and to titrating sedation will be required. Adjunctive interventions, including extracorporeal life support techniques, phrenic nerve stimulation, and clinical decision-support systems, may also play an important role in selected patients in the future. Evaluating the clinical impact of this new paradigm will be challenging, owing to the complexity of the intervention. The concept of lung- and diaphragm-protective ventilation presents a new opportunity to potentially improve clinical outcomes for critically ill patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".