Application of pulmonary ultrasound for respiratory failure in intensive care unit
Bibliographic record
Abstract
This review is part of a series of articles on the use of ultrasound in the intensive care unit. This review aims to demonstrate the most up-to-date ultrasound applications relevant to respiratory failure assessment and management in the intensive care unit. Pulmonary ultrasound encompasses pleural, parenchymal, and respiratory muscle ultrasound. It can be used at every stage of a patient’s course, including assessment of undifferentiated respiratory failure, disease-specific measurements and manipulations, respiratory muscle function assessment, lung and diaphragmatic protective ventilation, and liberation from mechanical ventilation. Multiple protocols and conceptual frameworks have been designed to assist the evaluation and management of undifferentiated patients. Disease states for which there is specific evidence include acute respiratory distress syndrome, cardiogenic pulmonary edema, bacterial pneumonia, and respiratory muscle dysfunction. Extensive outcome data supports the routine use of lung ultrasound in each situation. Pulmonary ultrasound has a large body of evidence supporting its widespread adoption within intensive care units. It represents the most versatile, non-invasive tool available for respiratory failure management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".