Optimal Ventilator Strategies in Acute Respiratory Distress Syndrome
Bibliographic record
Abstract
Mechanical ventilation practices in patients with acute respiratory distress syndrome (ARDS) have progressed with a growing understanding of the disease pathophysiology. Paramount to the care of affected patients is the delivery of lung-protective mechanical ventilation which prioritizes tidal volume and plateau pressure limitation. Lung protection can probably be further enhanced by scaling target tidal volumes to the specific respiratory mechanics of individual patients. The best procedure for selecting optimal positive end-expiratory pressure (PEEP) in ARDS remains uncertain; several relevant issues must be considered when selecting PEEP, particularly lung recruitability. Noninvasive ventilation must be used with caution in ARDS as excessively high respiratory drive can further exacerbate lung injury; newer modes of delivery offer promising approaches in hypoxemic respiratory failure. Airway pressure release ventilation offers an alternative approach to maximize lung recruitment and oxygenation, but clinical trials have not demonstrated a survival benefit of this mode over conventional ventilation strategies. Rescue therapy with high-frequency oscillatory ventilation is an important option in refractory hypoxemia. Despite a disappointing lack of benefit (and possible harm) in patients with moderate or severe ARDS, possibly due to lung hyperdistention and right ventricular dysfunction, high-frequency oscillation may improve outcome in patients with very severe hypoxemia.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".