Bibliographic record
Abstract
Acute-on-chronic liver failure (ACLF) is a newly recognized syndrome, characterized by acute deterioration of the patient's clinical status, usually precipitated by an acute event, associated with multiorgan failure and high short-term mortality. There are no uniform diagnostic criteria for ACLF; this is because of the different populations of patients with cirrhosis that predominate in different parts of the world. The pathogenesis of ACLF is related to a sudden burst of inflammation superimposed on a background of low-grade inflammation that is commonly present in advanced cirrhosis. Inflammation can lead to recruitment of various types of immune cells, which, when excessive, can cause circulatory compromise and tissue damage. It is important to avoid potential precipitating factors in susceptible patients with cirrhosis. Once ACLF develops, it is important to remove all precipitating factors and to treat individual organ failures. This is best done in the intensive care setting. Liver transplantation is the definitive treatment for ACLF, but this may not be appropriate for all patients, as the persistence of bacterial infection, functional failure of multiple organs, or severe deconditioning prior to liver transplant would render liver transplantation futile. Therefore, it is important to assess patients with ACLF for liver transplant early. Future directions will need to aim at establishing consensus diagnostic criteria for ACLF, and from there develop treatment strategies for these patients.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".