Efeito da manobra de recrutamento alveolar em pacientes com síndrome da angústia respiratória aguda: revisão sistemática e metanálise
Bibliographic record
Abstract
Purpose: To assess the effects of alveolar recruitment maneuvers (ARMs) on clinical outcomes in patients with acute respiratory distress syndrome (ARDS).Methods: We searched MEDLINE, EMBASE, LILACS, CINAHL, CENTRAL, Scopus, and Web of Science (from inception to July 2014) for randomized controlled trials (RCTs) evaluating the effects of ARMs versus no ARMs in adults with ARDS.We placed no language restriction on our search.Four teams of two reviewers independently assessed eligibility and risk of bias and extracted data from the included trials.We pooled data using random-effects model.We used trial sequential analysis (TSA) to establish monitoring boundaries to limit global type I error due to repetitive testing for our primary outcome (in-hospital mortality).We rated the quality of evidence using the GRADE system.Results: We included 10 RCTs (1594 patients, 612 events).The meta-analysis assessing the effect of ARMs on in-hospital mortality showed a risk ratio (RR) of 0.84 (95%CI 0.74-0.95;I 2 =0%), although quality of evidence was considered low due to the risk of bias in the included trials and indirectness of evidence, that is, ARMs were usually conducted along with other ventilatory interventions that may affect the outcome of interest.There were no differences in the rates of barotrauma (RR 1.11, 95%CI 0.78-1.57;I 2 =0%) or need for rescue therapies (RR 0.76, 95%CI 0.41-1.40;I 2 =56%).Most trials found no difference between groups regarding the duration of mechanical ventilation, length of stay in ICU and in hospital.The TSA showed that the available evidence of the effect of ARMs on in-hospital mortality is precise when considering a type I error of 5% but not when considering a type I error of 1%. Conclusions: Although ARMs may decrease mortality of patients with ARDSwithout increasing the risk for major adverse events, the current evidence is not definitive.Large-scale ongoing trials addressing this question may better inform clinical practice.
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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 | 0.031 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".