Acute Liver Failure: Biomarkers Evaluated by the Acute Liver Failure Study Group
Bibliographic record
Abstract
There has been a growing interest in identifying prognostic biomarkers that alone or with available prognostic models (King's College Criteria, KCC; MELD and ALFSG Prognostic Index) would improve prognosis in acute liver failure (ALF) patients being assessed for liver transplantation. The Acute Liver Failure Study Group (ALFSG) has evaluated 15 potential prognostic biomarkers: serum AFP; apoptosis-associated proteins; serum actin-free Gc-globulin; serum glycodeoxycholic acid; sRAGE/RAGE ligands; plasma osteopontin; circulating MBL, M-, L-, H-ficolin and CL-1; plasma galectin-9; serum FABP1; serum Lct2; miRNAs; factor V; thrombocytopenia, and sCD163. The ALFSG also has reported on 4 susceptibility biomarkers: keratins 8 and 18 (K8/K18) gene variants; polymorphisms of genes encoding putative APAP-metabolizing enzymes ( UGT1A1 , UGT 1A0 , UGT 2B15 , SULT1A1 , CYP2E1 , and CYP3A5 ) as well as CD44 and BHMT1 ; single nucleotide polymorphisms (SNPs) of genes associated with human behavior, rs2282018 in the arginine vasopressin ( AVP ) gene and rs11174811 in the AVP receptor 1A gene. Finally, rs2277680 of the CSCL16 gene in HBV-ALF patients. In conclusion, we have reviewed the prognostic and susceptibility biomarkers studied by the ALFSG. We suggest that a better approach to predicting the clinical outcome of an ALF patient will require a combination of biomarkers of pathogenic processes such as cell death, hepatic regeneration, and degree of inflammation that could be incorporated into prognostic models such as KCC, MELD or ALFSG PI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".