Management of the acute on chronic liver failure in the intensive care unit
Bibliographic record
Abstract
Acute on chronic liver failure (ACLF) reflects the development of organ failure(s) in a patient with cirrhosis and is associated with high short-term mortality. Given that ACLF has many different 'phenotypes', medical management needs to take into account the relationship between precipitating insult, organ systems involved and underlying physiology of chronic liver disease/cirrhosis. The goals of intensive care management of patients suffering ACLF are to rapidly recognize and treat inciting events (e.g. infection, severe alcoholic hepatitis and bleeding) and to aggressively support failing organ systems to ensure that patients may successfully undergo liver transplantation or recovery. Management of these patients is complex since they are prone to develop new organ failures and infectious or bleeding complications. ICU therapy parallels that applied in the general ICU population in some complications but differs in others. Given that liver transplantation in ACLF is an emerging and evolving field, multidisciplinary teams with expertise in critical care and transplant medicine best accomplish management of the critically ill ACLF patient. The focus of this review is to identify the common complications of ACLF and to describe the proper management in critically ill patients awaiting liver transplantation in our centres, including organ support, prognostic assessment and how to assess when recovery is unlikely.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".