Glycodeoxycholic Acid Levels as Prognostic Biomarker in Acetaminophen-Induced Acute Liver Failure Patients
Bibliographic record
Abstract
Acetaminophen (APAP)-induced acute liver failure (ALF) remains a major clinical problem. Although a majority of patients recovers after severe liver injury, a subpopulation of patients proceeds to ALF. Bile acids are generated in the liver and accumulate in blood during liver injury, and as such, have been proposed as biomarkers for liver injury and dysfunction. The goal of this study was to determine whether individual bile acid levels could determine outcome in patients with APAP-induced ALF (AALF). Serum bile acid levels were measured in AALF patients using mass spectrometry. Bile acid levels were elevated 5-80-fold above control values in injured patients on day 1 after the overdose and decreased over the course of hospital stay. Interestingly, glycodeoxycholic acid (GDCA) was significantly increased in non-surviving AALF patients compared with survivors. GDCA values obtained at peak alanine aminotransferase (ALT) and from day 1 of admission indicated GDCA could predict survival in these patients by receiver-operating characteristic analysis (AUC = 0.70 for day 1, AUC = 0.68 for peak ALT). Of note, AALF patients also had significantly higher levels of serum bile acids than patients with active cholestatic liver injury. These data suggest measurements of GDCA in this patient cohort modestly predicted outcome and may serve as a prognostic biomarker. Furthermore, accumulation of bile acids in serum or plasma may be a result of liver cell dysfunction and not cholestasis, suggesting elevation of circulating bile acid levels may be a consequence and not a cause of liver injury.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".