Patient self-inflicted lung injury and positive end-expiratory pressure for safe spontaneous breathing
Bibliographic record
Abstract
PURPOSE OF REVIEW: The potential risks of spontaneous effort and their prevention during mechanical ventilation is an important concept for clinicians and patients. The effort-dependent lung injury has been termed 'patient self-inflicted lung injury (P-SILI)' in 2017. As one of the potential strategies to render spontaneous effort less injurious in severe acute respiratory distress syndrome (ARDS), the role of positive end-expiratory pressure (PEEP) is now discussed. RECENT FINDINGS: Experimental and clinical data indicate that vigorous spontaneous effort may worsen lung injury, whereas, at the same time, the intensity of spontaneous effort seems difficult to control when lung injury is severe. Experimental studies found that higher PEEP strategy can be effective to reduce lung injury from spontaneous effort while maintaining some muscle activity. The recent clinical trial to reevaluate systemic early neuromuscular blockade in moderate-severe ARDS (i.e., reevaluation of systemic early neuromuscular blockade (ROSE) trial) support that a higher PEEP strategy can facilitate 'safe' spontaneous breathing under the light sedation targets (i.e., no increase in barotrauma nor 90 days mortality versus early muscle paralysis). SUMMARY: To prevent P-SILI in ARDS, it seems feasible to facilitate 'safe' spontaneous breathing in patients using a higher PEEP strategy in severe ARDS.
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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 | 0.002 |
| 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) | 0.005 | 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".