Noninvasive Ventilation for Patients Presenting With Acute Respiratory Failure: The Randomized Controlled Trials
Bibliographic record
Abstract
Noninvasive ventilation (NIV) in patients with acute respiratory failure (ARF), which was originally described decades ago, underwent a rebirth after reports of successful use in 1989. Over the following 18 years the literature on NIV has grown substantially. This paper summarizes the randomized controlled trials (RCTs) on NIV for acute respiratory failure. We conducted an extensive literature search and selected RCTs from that search. The results are presented primarily by etiology of respiratory failure, but we also include a short section on NIV for ARF in immunocompromised patients. The latter studies included patients with various etiologies of respiratory failure but with the common comorbidity of immunocompromise. Most of the RCTs have studied NIV for exacerbation of chronic obstructive pulmonary disease (COPD) or cardiogenic pulmonary edema. In general the RCTs have been small and used endotracheal intubation or NIV failure rate as primary outcomes. We conclude that NIV for ARF is supported by strong evidence from patients with COPD, but there is only weak support for NIV in other patient groups, such as immunocompromised patients. For other groups, such as patients with asthma, pneumonia, or acute lung injury, RCT-level evidence is lacking or does not suggest benefit. Clearly, major gaps remain in our evidence base.
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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 | 0.026 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".