The future of driving pressure: a primary goal for mechanical ventilation?
Bibliographic record
Abstract
BACKGROUND: Management of patients with acute respiratory distress syndrome (ARDS) remains supportive with lung protective mechanical ventilation. In this article, we discuss the physiological concept of driving pressure, current data, ongoing trials, and future directions needed to clarify the role of driving pressure in patients with ARDS. BODY: Driving pressure is the plateau airway pressure minus PEEP. It can also be expressed as the ratio of tidal volume to respiratory system compliance, indicating the decreased functional size of the lung observed in patients with ARDS (i.e., baby lung). Driving pressure as a strong predictor of mortality in patients with ARDS is supported by a post hoc analysis of previous randomized controlled trials and a subsequent meta-analysis. Importantly, the meta-analysis suggested targeting driving pressure below 13-15 cmH2O. Ongoing clinical trials of driving pressure in patients with ARDS focus mainly on physiological rather than clinical outcome but will provide important insights for the design of future clinical trials. CONCLUSION: Currently, no definite clinical recommendations on the routine use of driving pressure in patients with ARDS can be made, as the available data are hypothesis-generating. Randomized controlled trials are needed to evaluate the efficacy of a driving pressure-based ventilation strategy.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".