Effect of Lowering V <scp>t</scp> on Mortality in Acute Respiratory Distress Syndrome Varies with Respiratory System Elastance
Bibliographic record
Abstract
Abstract Rationale If the risk of ventilator-induced lung injury in acute respiratory distress syndrome (ARDS) is causally determined by driving pressure rather than by Vt, then the effect of ventilation with lower Vt on mortality would be predicted to vary according to respiratory system elastance (Ers). Objectives To determine whether the mortality benefit of ventilation with lower Vt varies according to Ers. Methods In a secondary analysis of patients from five randomized trials of lower- versus higher-Vt ventilation strategies in ARDS and acute hypoxemic respiratory failure, the posterior probability of an interaction between the randomized Vt strategy and Ers on 60-day mortality was computed using Bayesian multivariable logistic regression. Measurements and Main Results Of 1,096 patients available for analysis, 416 (38%) died by Day 60. The posterior probability that the mortality benefit from lower-Vt ventilation strategies varied with Ers was 93% (posterior median interaction odds ratio, 0.80 per cm H2O/[ml/kg]; 90% credible interval, 0.63–1.02). Ers was classified as low (&lt;2 cm H2O/[ml/kg], n = 321, 32%), intermediate (2–3 cm H2O/[ml/kg], n = 475, 46%), and high (&gt;3 cm H2O/[ml/kg], n = 224, 22%). In these groups, the posterior probabilities of an absolute risk reduction in mortality ≥ 1% were 55%, 82%, and 92%, respectively. The posterior probabilities of an absolute risk reduction ≥ 5% were 29%, 58%, and 82%, respectively. Conclusions The mortality benefit of ventilation with lower Vt in ARDS varies according to elastance, suggesting that lung-protective ventilation strategies should primarily target driving pressure rather than Vt.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".