Ventilatory Variables and Mechanical Power in Patients with Acute Respiratory Distress Syndrome
Bibliographic record
Abstract
Abstract Rationale Mortality in acute respiratory distress syndrome (ARDS) has decreased after the adoption of lung-protective strategies. Lower Vt, lower driving pressure (ΔP), lower respiratory rates (RR), and higher end-expiratory pressure have all been suggested as key components of lung protection strategies. A unifying theoretical explanation has been proposed that attributes lung injury to the energy transfer rate (mechanical power) from the ventilator to the patient, calculated from a combination of several ventilator variables. Objectives To assess the impact of mechanical power on mortality in patients with ARDS as compared with that of primary ventilator variables such as the ΔP, Vt, and RR. Methods We obtained data on ventilatory variables and mechanical power from a pooled database of patients with ARDS who had participated in six randomized clinical trials of protective mechanical ventilation and one large observational cohort of patients with ARDS. The primary outcome was mortality at 28 days or 60 days. Measurements and Main Results We included 4,549 patients (38% women; mean age, 55 ± 23 yr). The average mechanical power was 0.32 ± 0.14 J · min−1 · kg−1 of predicted body weight, the ΔP was 15.0 ± 5.8 cm H2O, and the RR was 25.7 ± 7.4 breaths/min. The driving pressure, RR, and mechanical power were significant predictors of mortality in adjusted analyses. The impact of the ΔP on mortality was four times as large as that of the RR. Conclusions Mechanical power was associated with mortality during controlled mechanical ventilation in ARDS, but a simpler model using only the ΔP and RR was equivalent.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".