Prehospital Noninvasive Ventilation for Acute Respiratory Failure: Systematic Review, Network Meta‐analysis, and Individual Patient Data Meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: This meta-analysis aimed to determine the effectiveness of prehospital continuous positive airway pressure (CPAP) or bilevel inspiratory positive airway pressure (BiPAP) in acute respiratory failure. METHODS: Fourteen electronic databases and research registers were searched from inception to August 2013. Randomized or quasi-randomized controlled trials that reported mortality or intubation rate for prehospital CPAP or BiPAP were selected and compared to a relevant comparator in patients with acute respiratory failure. An aggregate data network meta-analysis was used to jointly estimate intervention effects relative to standard care. A network meta-analysis using a mixture of individual patient-level data and aggregate data was carried out to assess potential treatment effect modifiers. RESULTS: Eight randomized and two quasi-randomized controlled trials (six CPAP, four BiPAP, sample sizes 23 to 207) were identified. The aggregate data network meta-analysis suggested that CPAP was the most effective treatment in terms of mortality (probability = 0.989) and intubation rate (probability = 0.639) and reduced both mortality (odds ratio [OR] = 0.41; 95% credible interval [CrI] = 0.20 to 0.77) and intubation rate (OR = 0.32; 95% CrI = 0.17 to 0.62), compared to standard care. The effect of BiPAP on mortality (OR = 1.94; 95% CrI = 0.65 to 6.14) and intubation rate (OR = 0.40; 95% CrI = 0.14 to 1.16) was uncertain. The network meta-analysis using individual patient-level data and aggregate data suggested that sex was a modifier of the effect of treatment on mortality. CONCLUSIONS: Prehospital CPAP can reduce mortality and intubation rates compared to standard care, while the effectiveness of prehospital BiPAP is uncertain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.046 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".