Diaphragm-protective mechanical ventilation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Diaphragm dysfunction is common in mechanically ventilated patients and predisposes them to prolonged ventilator dependence and poor clinical outcomes. Mechanical ventilation is a major cause of diaphragm dysfunction in these patients, raising the possibility that diaphragm dysfunction might be prevented if mechanical ventilation can be optimized to avoid diaphragm injury - a concept referred to as diaphragm-protective ventilation. This review surveys the evidence supporting the concept of diaphragm-protective ventilation and introduces potential routes and challenges to pursuing this strategy. RECENT FINDINGS: Mechanical ventilation can cause diaphragm injury (myotrauma) by a variety of mechanisms. An understanding of these various mechanisms raises the possibility of a new approach to ventilatory management, a diaphragm-protective ventilation strategy. Deranged inspiratory effort is the main mediator of diaphragmatic myotrauma; titrating ventilation to maintain an optimal level of inspiratory effort may help to limit diaphragm dysfunction and accelerate liberation of mechanical ventilation. SUMMARY: Mechanical ventilation can cause diaphragm injury and weakness. A novel diaphragm-protective ventilation strategy, avoiding the harmful effects of both excessive and insufficient inspiratory effort, has the potential to substantially improve outcomes for patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".