Cranial Ultrasound as a First-Line Imaging Examination for Craniosynostosis
Bibliographic record
Abstract
BACKGROUND: Radiography, typically the first-line imaging study for diagnosis of craniosynostosis, exposes infants to ionizing radiation. We aimed to compare the accuracy of cranial ultrasound (CUS) with radiography for the diagnosis or exclusion of craniosynostosis. METHODS: Children aged 0 to 12 months who were assessed for craniosynostosis during 2011-2013 by using 4-view skull radiography and CUS of the sagittal, coronal, lambdoid, and metopic sutures were included in this prospective study. Institutional review board approval and parental informed consent were obtained. CUS and radiography were interpreted independently and blindly by 2 pediatric radiologists; conflicts were resolved in consensus. Sutures were characterized as closed, normal, or indeterminate. Correlation between CUS and radiography and interreader agreement were examined for each suture. RESULTS: A total of 126 children (82 boys, 64.5%) ages 8 to 343 days were included. All sutures were normal on CUS and radiography in 115 patients (93.7%); craniosynostosis of 1 suture was detected in 8 (6.3%, 5 sagittal, 2 metopic, 1 coronal). In 3 cases the metopic suture was closed (n = 2) or indeterminate on CUS (n = 1) but normally closed on radiography. CUS sensitivity was 100%, specificity 98% (95% confidence interval 94%-100%). Reader agreement was 100% for sagittal, coronal, and lambdoid sutures (κ = 0.80); after consensus, disagreement remained on 3 metopic sutures. CONCLUSIONS: In this series, CUS could be safely used as a first-line imaging tool in the investigation of craniosynostosis, reducing the need for radiographs in young children. Additional assessment may be required for accurate assessment of the metopic suture.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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".