Acute Respiratory Distress Syndrome: Respiratory Monitoring and Pulmonary Physiology
Bibliographic record
Abstract
The high prevalence of acute respiratory distress syndrome (ARDS), its morbidity and mortality continue to fare a huge burden in the intensive care unit. More than 40 years ago, experimental studies have highlighted that, albeit essential, mechanical ventilation could be harmful to lungs and more recently to the diaphragm. Despite life-saving advances in mechanical ventilation (such as low tidal-volume ventilation, neuromuscular blockers agents, or prone positioning), a recent international observational study reported that most ARDS patients were not appropriately monitored. The monitoring capabilities of ventilators, in particular the simple interaction of the patient and the mechanical ventilation, are very powerful but are underutilized. This lack of monitoring may contribute to the persisting poor outcome of patients with ARDS. Providing a more careful ventilation is a priority to improve patients' outcomes. To achieve this goal, it is of paramount importance to better understand the complex relationship between the patient and the ventilator: the impact of ventilator settings on lungs during passive controlled ventilation, but also of patient's breathing efforts on lungs during assisted ventilation. In this review we present available tools to monitor respiratory mechanics at the bedside aiming at optimizing and personalizing mechanical ventilation. Hopefully, this careful management can decrease mortality of patients with ARDS in the future.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".