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Record W4365443572 · doi:10.1097/ccm.0000000000005824

Guidelines for the Management of Adult Acute and Acute-on-Chronic Liver Failure in the ICU: Neurology, Peri-Transplant Medicine, Infectious Disease, and Gastroenterology Considerations

2023· article· en· W4365443572 on OpenAlexaff
Rahul Nanchal, Ram Subramanian, Waleed Alhazzani, Joanna C. Dionne, William Peppard, Kai Singbartl, Jonathon D. Truwit, Ali Al‐Khafaji, Alley Killian, Mustafa Alquraini, Khalil Alshammari, Fayez Alshamsi, Emilie P. Belley‐Côté, Rodrigo Cartin‐Ceba, Steven M. Hollenberg, Dragos Galusca, David T. Huang, Robert C. Hyzy, Mats Junek, Prem Kandiah, Gagan Kumar, Rebecca L. Morgan, Peter E. Morris, Jody C. Olson, Rita Sieracki, Randolph H. Steadman, Beth Taylor, Constantine Karvellas

Bibliographic record

VenueCritical Care Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of AlbertaImpactOntario Clinical Oncology GroupMcMaster University
Fundersnot available
KeywordsMedicineNeurologyChronic liver diseaseInternal medicineLiver transplantationIntensive care medicineInfectious disease (medical specialty)DiseasePeriGastroenterologyTransplantationCirrhosis

Abstract

fetched live from OpenAlex

OBJECTIVES: To develop evidence-based recommendations for clinicians caring for adults with acute liver failure (ALF) or acute on chronic liver failure (ACLF) in the ICU. DESIGN: The guideline panel comprised 27 members with expertise in aspects of care of the critically ill patient with liver failure or methodology. We adhered to the Society of Critical Care Medicine standard operating procedures manual and conflict-of-interest policy. Teleconferences and electronic-based discussion among the panel, as well as within subgroups, served as an integral part of the guideline development. INTERVENTIONS: In part 2 of this guideline, the panel was divided into four subgroups: neurology, peri-transplant, infectious diseases, and gastrointestinal groups. We developed and selected Population, Intervention, Comparison, and Outcomes (PICO) questions according to importance to patients and practicing clinicians. For each PICO question, we conducted a systematic review and meta-analysis where applicable. The quality of evidence was assessed 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: We report 28 recommendations (from 31 PICO questions) on the management ALF and ACLF in the ICU. Overall, five were strong recommendations, 21 were conditional recommendations, two were best-practice statements, and we were unable to issue a recommendation for five questions due to insufficient evidence. CONCLUSIONS: Multidisciplinary, international experts formulated evidence-based recommendations for the management ALF and ACLF patients in the ICU, acknowledging that most recommendations were based on low quality and 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 imitation

Not 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.

metaresearch head score (Codex)0.036
metaresearch head score (Gemma)0.135
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.135
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.008
Bibliometrics0.0100.007
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0080.003
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.030
GPT teacher head0.338
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations28
Published2023
Admission routes1
Has abstractyes

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