From fructose to the future: liver disease biomarkers and their prognostic value in acute liver failure
Bibliographic record
Abstract
Acute liver failure (ALF) is an uncommon but severe condition with high morbidity and mortality. Advances in supportive care have improved patient outcomes, but liver transplantation remains the only life-saving intervention in many cases. Unfortunately, healthy donor livers are in short supply. In addition, transplant recipients face several potentially fatal risks including organ rejection, biliary and vascular complications, and infection. It is therefore critical to accurately identify patients who need a new liver while sparing those who do not. This also needs to be done quickly, within the first few days of hospital admission, due to the rapid progression of ALF. Prognostic tools, like the Clichy criteria, the King's College Criteria (KCC), the model for end-stage liver disease (MELD) score, the Acute Liver Failure Study Group Prognostic Index (ALFSGPI), and others have been available for this purpose since at least the 1980s and are commonly used today, but their performance is imperfect, leading to many efforts over the last several decades - and especially in recent years - to identify new noninvasive biomarkers. This review begins with a description of the earliest liver function (e.g. the levulose [fructose] test) and liver injury (e.g. alkaline phosphatase [ALP] and alanine aminotransferase [ALT]) tests and continues through the most recent proposed biomarkers, with critical evaluation of the prognostic utility of each using the KCC and MELD as benchmarks for comparison. Overall, there is as-yet no single biomarker that clearly and consistently performs better than the latter tools, though many may modestly improve the performance of the KCC or MELD when used in combination with them. The search for a better, single biomarker is therefore likely to continue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".