Bone Density in Children With Chronic Liver Disease Correlates With Growth and Cholestasis
Bibliographic record
Abstract
Osteopenia and bone fractures are significant causes of morbidity in children with cholestatic liver disease. Dual-energy X-ray absorptiometry (DXA) analysis was performed in children with intrahepatic cholestatic diseases who were enrolled in the Longitudinal Study of Genetic Causes of Intrahepatic Cholestasis in the Childhood Liver Disease Research Network. DXA was performed on participants aged >5 years (with native liver) diagnosed with bile acid synthetic disorder (BASD), alpha-1 antitrypsin deficiency (A1AT), chronic intrahepatic cholestasis (CIC), and Alagille syndrome (ALGS). Weight, height, and body mass index Z scores were lowest in CIC and ALGS. Total bilirubin (TB) and serum bile acids (SBA) were highest in ALGS. Bone mineral density (BMD) and bone mineral content (BMC) Z scores were significantly lower in CIC and ALGS than in BASD and A1AT (P < 0.001). After anthropometric adjustment, bone deficits persisted in CIC but were no longer noted in ALGS. In ALGS, height-adjusted and weight-adjusted subtotal BMD and BMC Z scores were negatively correlated with TB (P < 0.001) and SBA (P = 0.02). Mean height-adjusted and weight-adjusted subtotal BMC Z scores were lower in ALGS participants with a history of bone fractures. DXA measures did not correlate significantly with biliary diversion status. Conclusion: CIC patients had significant bone deficits that persisted after adjustment for height and weight and generally did not correlate with degree of cholestasis. In ALGS, low BMD and BMC reference Z scores were explained by poor growth. Anthropometrically adjusted DXA measures in ALGS correlate with markers of cholestasis and bone fracture history. Reduced bone density in this population is multifactorial and related to growth, degree of cholestasis, fracture vulnerability, and contribution of underlying genetic etiology.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".