Non-invasive ventilation for acute hypoxemic respiratory failure, including COVID-19
Bibliographic record
Abstract
Optimal initial non-invasive management of acute hypoxemic respiratory failure (AHRF), of both coronavirus disease 2019 (COVID-19) and non-COVID-19 etiologies, has been the subject of significant discussion. Avoidance of endotracheal intubation reduces related complications, but maintenance of spontaneous breathing with intense respiratory effort may increase risks of patients' self-inflicted lung injury, leading to delayed intubation and worse clinical outcomes. High-flow nasal oxygen is currently recommended as the optimal strategy for AHRF management for its simplicity and beneficial physiological effects. Non-invasive ventilation (NIV), delivered as either pressure support or continuous positive airway pressure via interfaces like face masks and helmets, can improve oxygenation and may be associated with reduced endotracheal intubation rates. However, treatment failure is common and associated with poor outcomes. Expertise and knowledge of the specific features of each interface are necessary to fully exploit their potential benefits and minimize risks. Strict clinical and physiological monitoring is necessary during any treatment to avoid delays in endotracheal intubation and protective ventilation. In this narrative review, we analyze the physiological benefits and risks of spontaneous breathing in AHRF, and the characteristics of tools for delivering NIV. The goal herein is to provide a contemporary, evidence-based overview of this highly relevant topic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".