Setting positive end-expiratory pressure: does the ‘best compliance’ concept really work?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Determining the optimal positive end-expiratory pressure (PEEP) setting remains a central yet debated issue in the management of acute respiratory distress syndrome (ARDS).The 'best compliance' strategy set the PEEP to coincide with the peak respiratory system compliance (or 2 cmH 2 O higher) during a decremental PEEP trial, but evidence is conflicting. RECENT FINDINGS: The physiological rationale that best compliance is always representative of functional residual capacity and recruitment has raised serious concerns about its efficacy and safety, due to its association with increased 28-day all-cause mortality in a randomized clinical trial in ARDS patients.Moreover, compliance measurement was shown to underestimate the effects of overdistension, and neglect intra-tidal recruitment, airway closure, and the interaction between lung and chest wall mechanics, especially in obese patients. In response to these concerns, alternative approaches such as recruitment-to-inflation ratio, the nitrogen wash-in/wash-out technique, and electrical impedance tomography (EIT) are gaining attention to assess recruitment and overdistention more reliably and precisely. SUMMARY: The traditional 'best compliance' strategy for determining optimal PEEP settings in ARDS carries risks and overlooks some key physiological aspects. The advent of new technologies and methods presents more reliable strategies to assess recruitment and overdistention, facilitating personalized approaches to PEEP optimization.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".