Liver Ultrasound Patterns in Children With Cystic Fibrosis Correlate With Noninvasive Tests of Liver Disease
Bibliographic record
Abstract
OBJECTIVES: Early identification of children with cystic fibrosis (CF) at risk for severe liver disease (CFLD) would enable targeted study of preventative therapies. There is no gold standard test for CFLD. Ultrasonography (US) is used to identify CFLD, but with concerns for its diagnostic accuracy. We aim to determine if differences in standard blood tests, imaging variables and noninvasive liver fibrosis indices correlate with liver US patterns, and thus provide supportive evidence that a heterogeneous US liver pattern reflects clinically relevant liver disease. METHODS: We studied baseline research abdominal US and bloodwork from 244 children with pancreatic insufficient CF, ages 3 to 12 years, enrolled in a prospective study of the ability of US to predict CF cirrhosis (PUSH study). Children with a heterogeneous (HTG) liver pattern on US (n = 62) were matched 1 : 2 in design with children with normal US (NL, n = 122). Analyses included children with nodular (NOD, n = 22) and homogeneous hyperechoic (HMG, n = 38) livers. RESULTS: Univariate analysis showed significant differences between US groups for standard blood tests, spleen size, and noninvasive liver fibrosis indices. Multivariable models discriminated NOD versus NL with excellent accuracy (AUROC 0.96). Models also distinguish HTG versus NL (AUROC 0.76), NOD versus HTG (0.78), and HMG versus NL (0.79). CONCLUSIONS: Liver US patterns in children with CF correlate with platelet count, spleen size and indices of liver fibrosis. Multivariable models of these biomarkers have excellent discriminating ability for NL versus NOD, and good ability to distinguish other US patterns, suggesting that US patterns correlate with clinically relevant liver disease.
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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 | 0.005 |
| 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 | 0.001 |
| 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".