Lung Ultrasound to Monitor Disease Severity and Aid Prognostication in COVID-19 Pneumonia: A Retrospective Analysis of Serial Lung Ultrasound Assessments
Bibliographic record
Abstract
Background: The aim of this retrospective analysis was to assess if serial lung ultrasound assessments in patients with COVID-19 pneumonia, including a novel simplified scoring system, correlate with PaO2:FiO2 ratio, as a marker of disease severity, and patient outcomes. Methods: Patients treated for COVID-19 pneumonia in a tertiary intensive care unit who had a lung ultrasound assessment were included. Standardised assessments of anterior and lateral lung regions were prospectively recorded. A validated lung ultrasound score-of-aeration and a simplified scoring system based on the number of disease-free lung regions were correlated with: PaO2:FiO2 ratio, successful weaning from mechanical ventilation, and status (alive or dead) at discharge. MedCalc© statistical software was used for statistical analysis. Results: 28 patients (109 assessments) were included. Correlation was seen between score-of-aeration and PaO2:FiO2 ratio (r = -0.61, p<0.0001) and between the simplified scoring system and PaO2:FiO2 ratio (r = 0.52 p<0.0001). Achieving a score-of-aeration of ≤9/24 or ≥2 disease-free regions was associated with successful weaning from mechanical ventilation and survival to ICU discharge (accuracy of 94% and 97% respectively). Conclusion: Retrospective analysis from this small cohort of patients demonstrates that scores-of-aeration and a simplified scoring system based on the number of disease-free antero-lateral regions from serial LUS assessments correlate with PaO2:FiO2 ratio as a marker of disease severity in patients with COVID-19 pneumonia. In addition, lung ultrasound may help identify patients who will have favourable outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".