Mechanical Ventilation to Minimize Progression of Lung Injury in Acute Respiratory Failure
Bibliographic record
Abstract
Mechanical ventilation is used to sustain life in patients with acute respiratory failure. A major concern in mechanically ventilated patients is the risk of ventilator-induced lung injury, which is partially prevented by lung-protective ventilation. Spontaneously breathing, nonintubated patients with acute respiratory failure may have a high respiratory drive and breathe with large tidal volumes and potentially injurious transpulmonary pressure swings. In patients with existing lung injury, regional forces generated by the respiratory muscles may lead to injurious effects on a regional level. In addition, the increase in transmural pulmonary vascular pressure swings caused by inspiratory effort may worsen vascular leakage. Recent data suggest that these patients may develop lung injury that is similar to the ventilator-induced lung injury observed in mechanically ventilated patients. As such, we argue that application of a lung-protective ventilation, today best applied with sedation and endotracheal intubation, might be considered a prophylactic therapy, rather than just a supportive therapy, to minimize the progression of lung injury from a form of patient self-inflicted lung injury. This has important implications for the management of these patients.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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 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".