Neonatal Dubin-Johnson Syndrome and its Differentiation from Biliary Atresia
Bibliographic record
Abstract
Background and Aims:The aim was to determine if liver biochemistry indices can be used as biomarkers to help differentiate patients with neonatal Dubin-Johnson syndrome (nDJS) from those with biliary atresia (BA).Methods: Patients with genetically-confirmed nDJS or cholangiographically confirmed BA were retrospectively enrolled and randomly assigned to discovery or verification cohorts.Their liver chemistries, measured during the neonatal period, were compared.Predictive values were calculated by receiver operating characteristic curve analysis.Results: A cohort of 53 nDJS patients was recruited, of whom 13 presented with acholic stools, and 14 underwent diagnostic cholangiography or needle liver biopsy to differentiate from BA. Thirty-five patients in the cohort, with complete biochemical information measured during the neonatal period, were compared with 133 infants with cholangiographically confirmed BA.Total and direct bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bile acids, alkaline phosphatase, and gamma-glutamyl transferase were significantly lower in nDJS than in BA.In the discovery cohort, the areas under the curve for ALT and AST were 0.908 and 0.943, respectively.In the validation cohort, 13/15 patients in the nDJS group were classified as nDJS, and 10/53 in the BA control group were positive (p<0.00001) with an ALT biomarker cutoff value of 75 IU/L.Thirteen of 15 patients were classified as nDJS and none were classified positive in the BA group (13/15 vs. 0/53, p<0.00001) with an AST cutoff of 87 IU/L.Conclusions: Having assembled and investigated the largest cohort of nDJS patients reported to date, we found that nDJS patients could be distinguished from BA patients using the serum AST level as a biomarker.The finding may be clinically useful to spare cholestatic nDJS patients unnecessary invasive procedures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".