Prevalence and outcomes of acute respiratory distress syndrome in patients with aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: In this study, we aimed to investigate the prevalence, timing, risk factors, and outcomes of acute respiratory distress syndrome (ARDS) in patients with aneurysmal subarachnoid hemorrhage (aSAH). Methods: PubMed and four other databases were searched for randomized controlled trials (RCTs) and observational studies of patients 18 years or older through October 20, 2021. Study quality was assessed, using the Cochrane Risk of Bias tool for RCTs and the Newcastle-Ottawa Scale for cohort and case-control studies. High-grade aSAH was defined as a Hunt-Hess grade≥3 and/or a modified Fisher score≥3. A good neurological outcome was defined as a Glasgow Outcome Scale score ≥4. Random-effects meta-analyses were conducted to estimate the pooled outcome prevalence and 95% confidence interval (CI). Results: Eleven observational studies (n=6,107) met the inclusion criteria. Overall, 15% of the patients (95% CI=10.5–20.0; I2=97.8%) developed ARDS after aSAH, with a mean time of 3 days (95% CI=1.9–3.7; I2=54%). Overall survival at discharge was 80% (95% CI=75–86; I2=96%), and 67% of aSAH patients (95% CI=54.9–78.9; I2=94%) had a good neurological outcome at any time. The aSAH cohort without ARDS had a higher rate of survival than those with ARDS (79% vs. 49%, P=0.028). Male sex, patients with a high-grade aSAH, patients who developed pneumonia, and systemic inflammatory response syndrome during hospital admission were at a higher risk of developing ARDS.Conclusion: In this meta-analysis, approximately one in six patients developed ARDS after aSAH, with a mean time of 3 days from the initial presentation, and ARDS was associated with increased mortality.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".