Unconjugated hyperbilirubinemia may exacerbate certain underlying chronic liver diseases
Bibliographic record
Abstract
BACKGROUND: Negative correlations have been described between elevated serum unconjugated bilirubin levels and the prevalence/severity of various chronic inflammatory conditions. Whether a similar association exists for patients with unconjugated hyperbilirubinemia (UCB) and underlying chronic liver diseases (CLD) has yet to be reported. The aim of this study was to document hepatic necro-inflammatory disease activity and fibrosis in CLD patients with and without UCB and otherwise normal liver function tests (albumin and INR). METHODS: Necro-inflammatory disease activity was assessed by serum aminotransferase levels and fibrosis by APRI and FIB-4 calculations. UCB patients were matched 1:2 by age, gender and underlying CLD to patients with normal bilirubin levels. RESULTS: From a database of 9,745 CLD patients, 208 (2.1%) had UCB and 399 served as matched controls. Overall, UCB patients had significantly higher serum aminotransferase levels, APRI and FIB-4 scores. The differences were driven by patients with underlying chronic viral or immune mediated liver disorders rather than non-alcoholic fatty liver disease, alcohol related liver disease, or ‘other’ CLDs. CONCLUSIONS: These results suggest UCB is associated with increased rather than decreased hepatic necro-inflammatory disease activity and fibrosis in patients with certain CLDs.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".