Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to investigate feasibility and evaluate test characteristics of bedside ultrasound for the detection of skull fractures in children with closed head injury (CHI). METHODS: This was a prospective, observational study conducted in a pediatric emergency department of an urban tertiary care children's hospital. A convenience sample of children younger than 18 years were enrolled if they presented with an acute CHI, and a computed tomography (CT) scan was performed. Ultrasound was performed by pediatric emergency medicine physicians with at least 1 month of training in bedside ultrasound. Ultrasound interpretation as either positive or negative for the presence of skull fracture was compared with attending radiologist CT scan dictation. Test characteristics (sensitivity, specificity, and positive and negative predictive values) were calculated. RESULTS: Forty-six patients were enrolled. The median age was 2 years (range, 2 months to 17 years). Eleven patients (24%) were diagnosed with skull fractures on CT scan. Bedside ultrasound had a sensitivity of 82% (95% confidence interval [CI], 48%-97%), specificity of 94% (95% CI, 79%-99%), positive predictive value of 82% (95% CI, 48%-97%), and negative predictive value of 94% (95% CI, 79%-99%). CONCLUSIONS: Bedside ultrasonography can be used by pediatric emergency medicine physicians to detect skull fractures in children with acute CHI. Larger studies are needed to validate these findings. Future studies should investigate the role of this modality as an adjunct to clinical decision rules to reduce unnecessary CT scans in the evaluation of acute CHI in children.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".