Lung Ultrasound and Sonographic Subpleural Consolidation in COVID-19 Pneumonia Correlate with Disease Severity
Bibliographic record
Abstract
Introduction. One of the ultrasonic features of COVID-19 pneumonia is the presence of subpleural consolidation (SPC), and the number of SPCs varies among patients with COVID-19 pneumonia. Aim. To examine the relationship between disease severity and the number of SPCs on admission. Methodology. This observational, prospective, single‐center study included patients with suspected COVID-19 infection who had been transferred to the ICU. A specialized intensivist in critical care ultrasound performed lung ultrasound (LUS) and echocardiography within 12 hours of a patient’s admission to the ICU. The aeration score was calculated, and the total number of SPCs was quantified in 12 zones of the LUS. Results. Of 109 patients with suspected COVID-19 pneumonia, 77 (71%) were confirmed. The median patient age was 53 (82–36) years, and 81 of the patients (73.7%) were men. The aeration score and the counts of subpleural consolidation in each zone were significantly higher in patients with COVID-19 pneumonia ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>P</a:mi> <a:mo>=</a:mo> <a:mn>0.018</a:mn> </a:math> and <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>P</c:mi> <c:mo><</c:mo> <c:mn>0.0001</c:mn> </c:math> , respectively). There was an inverse relationship between PO2/FiO2, the aeration score, and the number of subpleural consolidations. The higher the number of SPCs, the worse the PO2/FiO2 will be. Conclusions. Sonographic SPC counts correlate well with the severity of COVID-19 pneumonia and PO2/FiO2. The number of SPCs should be considered when using LUS to assess disease severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".