Impact of spontaneous breathing during mechanical ventilation in acute respiratory distress syndrome
Bibliographic record
Abstract
PURPOSE OF REVIEW: Facilitating spontaneous breathing has been traditionally recommended during mechanical ventilation in acute respiratory distress syndrome (ARDS). However, early, short-term use of neuromuscular blockade appears to improve survival, and spontaneous effort has been shown to potentiate lung injury in animal and clinical studies. The purpose of this review is to describe the beneficial and deleterious effects of spontaneous breathing in ARDS, explain potential mechanisms for harm, and provide contemporary suggestions for clinical management. RECENT FINDINGS: Gentle spontaneous effort can improve lung function and prevent diaphragm atrophy. However, accumulating evidence indicates that spontaneous effort may cause or worsen lung and diaphragm injury, especially if the ARDS is severe or spontaneous effort is vigorous. Recently, such effort-dependent lung injury has been termed patient self-inflicted lung injury (P-SILI). Finally, several approaches to minimize P-SILI while maintaining some diaphragm activity (e.g. partial neuromuscular blockade, high PEEP) appear promising. SUMMARY: We update and summarize the role of spontaneous breathing during mechanical ventilation in ARDS, which can be beneficial or deleterious, depending on the strength of spontaneous activity and severity of lung injury. Future studies are needed to determine ventilator strategies that minimize injury but maintaining some diaphragm activity.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".