Prospective observational study of point-of-care ultrasound for diagnosing pneumonia
Bibliographic record
Abstract
OBJECTIVES: The clinical diagnosis of pneumonia lacks specificity and may lead to antibiotic overuse, whereas radiological diagnoses can lack sensitivity. Point-of-care lung ultrasound is an emerging diagnostic tool. There are limited prospective data, however, on the accuracy of sonologists in the paediatric emergency department setting. We aimed to test the diagnostic accuracy of lung ultrasound for pneumonia using chest radiograph (CR) as the reference standard. METHODS: This prospective observational cohort study in a paediatric emergency department enrolled children aged 1 month to <18 years, who had a CR ordered for possible pneumonia. Lung ultrasounds were performed by two blinded sonologists with focused training. Sonographic pneumonia was defined as lung consolidation with air bronchograms. Radiograph and ultrasound results both required agreement between two readers, with final results determined by an arbiter in cases of disagreement. Patient management was decided by treating clinicians who were blinded to lung ultrasound results. Follow-up was performed by phone and medical record review to obtain final diagnosis and antibiotic use. RESULTS: Of 97 included patients, CR was positive for pneumonia in 44/97 (45%) and lung ultrasound was positive in 57/97 (59%). Ultrasound sensitivity was 91% (95% CI 78% to 98%) and specificity was 68% (95% CI 54% to 80%). Ultrasound results displayed greater consistency with CR and patient outcomes when sonographic consolidation exceeded 1 cm. Thirteen of 57 patients with sonographic consolidation improved without antibiotics. CONCLUSION: Lung ultrasound may have a role as first-line imaging in patients with possible pneumonia, with higher specificity for consolidations exceeding 1 cm. TRIAL REGISTRATION NUMBER: ACTRN12616000361404, http://www.ANZCTR.org.au/ACTRN12616000361404.aspx.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".