Early life predictive markers of liver disease outcome in an International, Multicentre Cohort of children with Alagille syndrome
Bibliographic record
Abstract
BACKGROUND & AIMS: Liver disease in Alagille syndrome is highly variable. Many of the patients presenting with severe cholestasis early in life improve spontaneously; 10-20%, however, have progressive disease. It is currently not possible to predict long-term hepatic outcomes in Alagille syndrome. This international, multicentre study was aimed at identifying early life predictors of liver disease outcome. METHODS: Retrospective clinical, laboratory and radiographic data from a cohort of 144 Alagille syndrome patients, whose long-term hepatic outcomes had been determined a priori based on previously published criteria, were collected. RESULTS: Sixty-seven patients had mild and 77 had severe hepatic outcome. Univariate analysis demonstrated that cholestasis and fibrosis on biopsy, as well as the presence of xanthomata were significantly different between the groups (P < 0.05 for all). Mixed model analysis revealed that total serum bilirubin and serum cholesterol were also associated with outcome (P = 0.001 and P = 0.002, respectively). Graphical representation of the data revealed a change in total bilirubin levels between 12 and 24 months of age in the mild group. Recursive partitioning identified a threshold for total bilirubin of 3.8 mg/dl (65 mmol/L) in that age-frame that differentiated between outcomes. A multivariable logistic regression model was developed using fibrosis, xanthomata and the total bilirubin cut-off of 3.8 mg/dl (65 mmol/L), which generated an area under the ROC curve of 0.792. CONCLUSIONS: The long-term hepatic outcomes of patients with Alagille syndrome can be predicted based on serum total bilirubin between the ages of 12-24 months combined with fibrosis on liver biopsy and the presence of xanthomata on physical examination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".