From bedside to bench: lung ultrasound for the assessment of pulmonary edema in animal models
Bibliographic record
Abstract
Traditionally, the lung has been excluded from the ultrasound organ repertoire and, hence, the application of lung ultrasound (LUS) was largely limited to a few enthusiastic clinicians. Yet, in the last decades, the recognition of the previously untapped diagnostic potential of LUS in intensive care medicine has fueled its widespread use as a rapid, non-invasive and radiation-free bedside approach with excellent diagnostic accuracy for many of the most common causes of acute respiratory failure, e.g., cardiogenic pulmonary edema, pneumonia, pleural effusion and pneumothorax. Its increased clinical use has also incited attention for the potential usefulness of LUS in preclinical studies with small animal models mimicking lung congestion and pulmonary edema formation. Application of LUS to small animal models of pulmonary edema may save time, is cost-effective, and may reduce the number of experimental animals due to the possibility of serial evaluations in the same animal as compared with traditional end-point measurements. This review provides an overview of the emerging field of LUS with a specific focus on its application in animal models and highlights future perspectives for LUS in preclinical research.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.002 |
| 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".