Association of different positive end-expiratory pressure selection strategies with all-cause mortality in adult patients with acute respiratory distress syndrome
Bibliographic record
Abstract
BACKGROUND: The acute respiratory distress syndrome (ARDS) has high morbidity and mortality. Positive end-expiratory pressure (PEEP) is commonly used in patients with ARDS but the best method to select the optimal PEEP level and reduce all-cause mortality is unclear. The primary objective of this network meta-analysis is to summarize the available evidence and to compare the effect of different PEEP selection strategies on all-cause mortality in adult patients with ARDS. METHODS: We will search MEDLINE, Cochrane Central Register of Controlled Trials, PubMed, EMBASE, and LILACS from inception onwards for randomized controlled trials assessing the effect of PEEP selection strategies in adult patients with moderate to severe ARDS. We will exclude studies that did not use a lung-protective ventilation approach as part of the comparator or intervention strategy. The primary outcome will be all-cause mortality (at the longest available follow-up and up to 90 days). Secondary outcomes will include barotrauma, ventilator-free days, intensive care unit and hospital length of stay, and changes in oxygenation. Two reviewers will independently screen all citations, full-text articles, and extract study-data. We will assess the risk of bias for each of the outcomes using version 2 of the Cochrane risk of bias tool for randomized controlled trials. If feasible, Bayesian network meta-analyses will be conducted to obtain pooled estimates of all potential head-to-head comparisons. We will report pairwise and network meta-analysis treatment effect estimates as risk ratios and risk differences, together with the associated 95% credible intervals. We will assess certainty in effect estimates using GRADE methodology. DISCUSSION: The present study will inform clinical decision-making for adult patients with ARDS and will improve our understanding of the limitations of the available literature assessing PEEP selection strategies. Finally, this information may also inform the design of future randomized trials, including the selection of interventions, comparators, and predictive enrichment strategies. TRIAL REGISTRATION: PROSPERO 2020 CRD42020193302 .
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.032 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".