Longitudinal Relationship Between Elevated Liver Biochemical Tests and Negative Clinical Outcomes in Primary Biliary Cholangitis: A Population‐Based Study
Bibliographic record
Abstract
BACKGROUND: Elevated liver biochemistries are associated with increased risk of negative outcomes in patients with primary biliary cholangitis (PBC). AIMS: To evaluate whether longitudinal monitoring of liver biochemistries and fibrosis scores provides additional prognostic value and to assess the relationship between the degree of elevation of multiple biomarkers within different alkaline phosphatase (ALP) strata. METHODS: Adults with PBC were identified from Komodo's Healthcare Map. A Cox proportional hazards model examined time to first occurrence of hospitalisation due to hepatic decompensation, liver transplantation, or death as a function of the proportion of time during follow-up that liver biochemistries and fibrosis scores exceeded thresholds. Within ALP strata (ALP ≤ upper limit of normal [ULN]; ALP>ULN to ≤ 1.67 × ULN; ALP > 1.67 × ULN), separate multivariate Cox hazard models assessed the association between time-varying covariates and the composite endpoint. RESULTS: Overall, 3974 patients were included; 88.2% were female, with a mean age of 59.4 years. The median follow-up was 2.5 years. Increasing magnitude and duration beyond established thresholds of ALP, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TB), AST/platelet ratio index (APRI) and fibrosis-4 (FIB-4) were associated with increased risk of negative outcomes. Elevated ALT, AST, TB, APRI and FIB-4 were associated with increased risk of negative outcomes across all ALP strata. CONCLUSIONS: Prolonged elevation of multiple hepatic biomarkers and fibrosis scores is associated with a greater risk of negative clinical outcomes, underscoring the importance of ongoing monitoring beyond the guideline-recommended initial treatment response to guide timely treatment decisions and improve PBC management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".