High-frequency oscillatory ventilation: A narrative review
Bibliographic record
Abstract
High-frequency oscillatory ventilation (HFOV) is a lung-protective strategy that can be utilized in the full spectrum of patient populations ranging from neonatal to adults with acute lung injury. HFOV is often utilized as a rescue strategy when conventional mechanical ventilation (CV) has failed. HFOV uses low tidal volumes and constant mean airway pressures in conjunction with high respiratory rates to provide beneficial effects on oxygenation and ventilation, while eliminating the traumatic "inflate-deflate" cycle imposed by CV. Although statistical evidence supporting HFOV is particularly low, potential benefits for its application in many clinical manifestations still remain. High-frequency oscillation is a safe and effective rescue mode of ventilation for the treatment of acute respiratory distress syndrome (ARDS). All patients who have ventilator-induced lung injury (VILI) or are at risk of developing VILI or ARDS would be suitable candidates for HFOV, especially those who have failed conventional mechanical ventilation. This narrative aims to provide a review of HFOV vis-à-vis its indications, contraindications, hazards, parameters to monitoring, patient selection, clinical goals, mechanisms of action, controls for optimizing ventilation and oxygenation, clinical application in ARDS, and a comparison with other modes of mechanical ventilation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".