High-frequency oscillatory ventilation and ventilator-induced lung injury
Bibliographic record
Abstract
INTRODUCTION: Although mechanical ventilation is lifesaving for patients with acute respiratory distress syndrome, it can cause ventilator-induced lung injury. To minimize ventilator-induced lung injury, different ventilatory strategies have been developed. One of the strategies is the use of high-frequency oscillatory ventilation (HFOV). THEORETICAL BACKGROUNDS OF VENTILATOR-INDUCED LUNG INJURY AND HFOV: Because of the novel gas exchange mechanisms, HFOV can provide adequate gas exchange using extremely small tidal volumes and maintain high end-expiratory lung volume without inducing overdistension, which should result in minimization of ventilator-induced lung injury. STUDIES OF HFOV AND LUNG INJURY: There are convincing clinical and animal data indicating that HFOV is an ideal lung-protective ventilatory strategy, particularly in the setting of neonatal respiratory failure, if lung volume recruitment is performed. CLINICAL IMPLICATION OF HFOV IN ADULT ACUTE RESPIRATORY DISTRESS SYNDROME: A recent clinical trial demonstrated early (<16 hrs) improvement in oxygenation with HFOV and a 30-day mortality of 37% with HFOV vs. 52% with pressure-controlled ventilation (p = .102), suggesting that HFOV is as effective and safe as the conventional strategy in adult acute respiratory distress syndrome. Future studies examining optimal algorithms of HFOV using clinically relevant animal models, and patients with acute respiratory distress syndrome, are imperative to determine whether the wide-spread application of HFOV is warranted in adult acute respiratory distress syndrome.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".