Airway Pressure Release Ventilation and High-Frequency Oscillatory Ventilation: Potential Strategies to Treat Severe Hypoxemia and Prevent Ventilator-Induced Lung Injury
Bibliographic record
Abstract
Although lifesaving, mechanical ventilation can itself be responsible for damage to lung parenchyma. This ventilator-induced lung injury is especially observed in already injured lungs of patients with ARDS. New ventilatory approaches are needed to safely treat patients with ARDS, and recent studies have suggested the potential utility of open-lung strategies. Airway pressure release ventilation (APRV) and high-frequency oscillatory ventilation (HFOV) are 2 different open-lung strategies that have been proposed to treat refractory hypoxemic respiratory failure while preventing ventilator-induced lung injury. APRV provides increased airway pressure as a potential recruitment mechanism and allows spontaneous breathing, with the potential benefits of decreased sedation, shorter duration of mechanical ventilation, and improvement in cardiac performance. HFOV delivers very small tidal volumes, to prevent volutrauma, at a constant (relatively high) mean airway pressure, thus avoiding atelectrauma. Despite their theoretical benefits, the utility of APRV and HFOV remains unproven and controversial for the routine treatment of ARDS in adult patients. This review is focused on the theoretical and practical aspects of APRV and HFOV, provides an overview of the current evidence, and addresses their possible use in the treatment of ARDS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.004 | 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".