Comparative Effectiveness of Protective Ventilation Strategies for Moderate and Severe Acute Respiratory Distress Syndrome. A Network Meta-Analysis
Bibliographic record
Abstract
Abstract Rationale Choosing the best ventilation strategy for acute respiratory distress syndrome (ARDS) is complex, yet it is highly relevant to clinicians during a respiratory pandemic. Objectives To compare the effects of low Vt, high Vt, high positive end-expiratory pressure (PEEP), prone ventilation, high-frequency oscillation, and venovenous extracorporeal membrane oxygenation (VV ECMO) on mortality in ARDS. Methods We performed a network meta-analysis of randomized trials. We applied the Grading of Recommendations Assessment, Development and Evaluation methodology to discern the relative effect of interventions on mortality. Measurements and Main Results We analyzed 34 trials including 9,085 adults with primarily moderate-to-severe ARDS (median baseline PaO2/FiO2, 118; interquartile range, 110–143). Prone positioning combined with low Vt was the best strategy (risk ratio [RR], 0.74 [95% confidence interval (CI), 0.60–0.92] vs. low Vt; high certainty). VV ECMO was also rated among the best (RR, 0.78 [95% CI, 0.58–1.05] vs. low Vt; RR, 0.66; [95% CI, 0.49–0.88] vs. high Vt) but was rated with lower certainty because VV ECMO was restricted to very severe ARDS (mean baseline PaO2/FiO2<75). High PEEP combined with low Vt was rated intermediately (RR, 0.91 [95% CI, 0.81–1.03] vs. low Vt; low certainty; RR, 0.77 [95% CI, 0.65–0.91] vs. high Vt; moderate certainty). High Vt was rated worst (RR, 1.19 [95% CI, 1.02–1.37] vs. low Vt; moderate certainty), and we found no support for high-frequency oscillation or high Vt with prone ventilation. Conclusions These findings suggest that combining low Vt with prone ventilation is associated with the greatest reduction in mortality for critically ill adults with moderate-to-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 | 0.027 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.045 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".