Guidelines for the Management of Adult Acute and Acute-on-Chronic Liver Failure in the ICU: Cardiovascular, Endocrine, Hematologic, Pulmonary, and Renal Considerations
Bibliographic record
Abstract
OBJECTIVES: To develop evidence-based recommendations for clinicians caring for adults with acute or acute on chronic liver failure in the ICU. DESIGN: The guideline panel comprised 29 members with expertise in aspects of care of the critically ill patient with liver failure and/or methodology. The Society of Critical Care Medicine standard operating procedures manual and conflict-of-interest policy were followed throughout. Teleconferences and electronic-based discussion among the panel, as well as within subgroups, served as an integral part of the guideline development. SETTING: The panel was divided into nine subgroups: cardiovascular, hematology, pulmonary, renal, endocrine and nutrition, gastrointestinal, infection, perioperative, and neurology. INTERVENTIONS: We developed and selected population, intervention, comparison, and outcomes questions according to importance to patients and practicing clinicians. For each population, intervention, comparison, and outcomes question, we conducted a systematic review aiming to identify the best available evidence, statistically summarized the evidence whenever applicable, and assessed the quality of evidence using the Grading of Recommendations Assessment, Development, and Evaluation approach. We used the evidence to decision framework to facilitate recommendations formulation as strong or conditional. We followed strict criteria to formulate best practice statements. MEASUREMENTS AND MAIN RESULTS: In this article, we report 29 recommendations (from 30 population, intervention, comparison, and outcomes questions) on the management acute or acute on chronic liver failure in the ICU, related to five groups (cardiovascular, hematology, pulmonary, renal, and endocrine). Overall, six were strong recommendations, 19 were conditional recommendations, four were best-practice statements, and in two instances, the panel did not issue a recommendation due to insufficient evidence. CONCLUSIONS: Multidisciplinary international experts were able to formulate evidence-based recommendations for the management acute or acute on chronic liver failure in the ICU, acknowledging that most recommendations were based on low-quality indirect evidence.
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 | 0.025 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".