Brain Point of Care Ultrasound in Young Children Receiving Computed Tomography in the Emergency Department: A Proof of Concept Study
Bibliographic record
Abstract
Background: Point of Care Ultrasound (POCUS) is an important tool in pediatric emergency medicine. In neonatal intensive care medicine ultrasound is often used to evaluate the brains of sick neonates. In theory, POCUS could be used in the ED in young children to evaluate the brain for abnormal pathology. Objectives: To examine the ability of PEM faculty to use brain POCUS to identify clinically significant brain injuries in children with head injuries and/or abnormal neurological exams, and generate sensitivity and specificity of brain POCUS in assessing such findings. Methods: This study used a convenience sample of patients seen in a tertiary care pediatric centre who required a CT head. A team of physicians who were trained at a workshop for brain POCUS were on call to perform the POCUS while being blinded to the results of the CT. Results: 21 children were enrolled in the study. Five (24%) of the patients had a CT that was positive for intracranial bleeds. Of the 5 patients with a positive CT, 3 had a brain POCUS scan that was also positive. The two false negative brain POCUS scans were on patients with small bleeds (no surgical intervention required) on CT, as reported by radiology. The sensitivity of brain POCUS was 60% (CI 15% - 95%) with a specificity of 94% (CI 70%-100%). The diagnostic accuracy of brain POCUS was 86% (CI 64% - 97%). Conclusion: This small proof of concept study shows that brain POCUS is an imaging modality with reasonable sensitivity and specificity in identifying intracranial pathologies that are present on CT. Its use may be most beneficial to expedite definitive imaging and subspeciality involvement.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".