Clinical strategies for implementing lung and diaphragm-protective ventilation: avoiding insufficient and excessive effort
Bibliographic record
Abstract
Mechanical ventilation may have adverse effects on both the lung and the diaphragm. Injury to the lung is mediated by excessive mechanical stress and strain, whereas the diaphragm develops atrophy as a consequence of low respiratory effort and injury in case of excessive effort. The lung and diaphragm-protective mechanical ventilation approach aims to protect both organs simultaneously whenever possible. This review summarizes practical strategies for achieving lung and diaphragm-protective targets at the bedside, focusing on inspiratory and expiratory ventilator settings, monitoring of inspiratory effort or respiratory drive, management of dyssynchrony, and sedation considerations. A number of potential future adjunctive strategies including extracorporeal CO 2 removal, partial neuromuscular blockade, and neuromuscular stimulation are also discussed. While clinical trials to confirm the benefit of these approaches are awaited, clinicians should become familiar with assessing and managing patients’ respiratory effort, based on existing physiological principles. To protect the lung and the diaphragm, ventilation and sedation might be applied to avoid excessively weak or very strong respiratory efforts and patient-ventilator dysynchrony.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".