Point‐of‐care lung ultrasound is more reliable than chest X‐ray for ruling out acute chest syndrome in sickle cell pediatric patients: A prospective study
Bibliographic record
Abstract
BACKGROUND: Acute chest syndrome (ACS) is a leading cause of morbidity and mortality in sickle cell patients, and it is often challenging to establish its diagnosis. PROCEDURE: This was a prospective observational study conducted in a pediatric emergency (PEM) department. We aimed to investigate the performance characteristics of point-of-care lung ultrasound (LUS) for diagnosing ACS in sickle cell children. LUS by trained PEM physicians was performed and interpreted as either positive or negative for consolidation. LUS results were compared to chest X-ray (CXR) and discharge diagnosis as reference standards. RESULTS: Four PEM physicians performed the LUS studies in 79 suspected ACS cases. The median age was 8 years (range 1-17 years). Fourteen cases (18%) received a diagnosis of ACS based on CXR and 21 (26.5%) had ACS discharge diagnosis. Comparing to CXR interpretation as the reference standard, LUS had a sensitivity of 100% (95% CI: 77%-100%), specificity of 68% (95% CI: 56%-79%), positive predictive value of 40% (95% CI: 24%-56%), and negative predictive value of 100% (95% CI: 92%-100%). Overall LUS accuracy was 73.42% (95% CI: 62%-83%). Using discharge diagnosis as the endpoint for both CXR and LUS, LUS had significantly higher sensitivity (100% vs. 62%, p = .0047) and lower specificity (76% vs.100%, p = .0002). LUS also had lower positive (60% vs.100%, p < .0001) and higher negative (100% vs.77%, p = .0025) predictive values. The overall accuracy was similar for both tests (82% vs. 88%, p = .2593). CONCLUSION: The high negative predictive value, with narrow CIs, makes LUS an excellent ruling-out tool for ACS.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".