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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".