Critical care management of the patient with cirrhosis awaiting liver transplant in the intensive care unit
Bibliographic record
Abstract
Patients with cirrhosis who are awaiting liver transplantation (LT) are at high risk for developing critical illnesses. Current liver allocation policies that dictate a "sickest first" approach coupled with a mismatch between need and availability of organs result in longer wait times, and thus, patients are becoming increasingly ill while awaiting organ transplantation. Even patients with well-compensated cirrhosis may suffer acute deterioration; the syndrome of acute-on-chronic liver failure (ACLF) results in multisystem organ dysfunction and a marked increase in associated short-term morbidity and mortality. For patients on transplant waiting lists, the development of multisystem organ failure may eliminate candidacy for transplant by virtue of being "too sick" to safely undergo transplantation surgery. The goals of intensive care management of patients suffering ACLF are to rapidly recognize and treat inciting events (eg, infection and bleeding) and to aggressively support failing organ systems to ensure that patients may successfully undergo LT. Management of the critically ill ACLF patient awaiting transplantation is best accomplished by multidisciplinary teams with expertise in critical care and transplant medicine. Such teams are well suited to address the needs of this unique patient population and to identify patients who may be too ill to proceed to transplantation surgery. The focus of this review is to identify the common complications of ACLF and to describe our approach management in critically ill patients awaiting LT in our centers. Liver Transplantation 23 1465-1476 2017 AASLD.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".