Point-of-care lung ultrasound imaging in pediatric COVID-19
Bibliographic record
Abstract
BACKGROUND: There has been limited data regarding the usefulness of lung ultrasound (US) in children with COVID-19. OBJECTIVE: To describe lung US imaging findings and aeration score of 34 children with COVID-19. METHODS: This study included 0-16-year-old patients with confirmed COVID-19, who were admitted between April 19 and June 18, 2020 in two hospitals in the city of Sao Paulo, Brazil. Lung US was performed as part of the routine evaluation by a skilled Pediatric Emergency physician. Clinical and laboratory data were collected and severity classifications were done according to an available clinical definition. The lung US findings were described for each lung field and a validated ultrasound lung aeration score was calculated. Data obtained was correlated with clinical information and other imaging modalities available for each case. RESULTS: Thirty-four confirmed COVID-19 patients had a lung US performed during this period. Eighteen (18/34) had abnormalities on the lung US, but eight of them (8/18) had a normal chest radiograph. Ultrasound lung aeration score medians for severe/critical, moderate, and mild disease were 17.5 (2-30), 4 (range 0-14), 0 (range 0-15), respectively (p = 0.001). Twelve patients (12/34) also had a chest computed tomography (CT) performed; both the findings and topography of lung compromise on the CT were consistent with the information obtained by lung US. CONCLUSION: Point-of-care lung US may have a key role in assessing lung injury in children with COVID-19.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".