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Record W3001519098 · doi:10.1097/mcc.0000000000000698

Critical care considerations in the management of acute-on-chronic liver failure

2020· review· en· W3001519098 on OpenAlexaff
Andrew MacDonald, Jody C. Olson, Constantine Karvellas

Bibliographic record

VenueCurrent Opinion in Critical Care · 2020
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDecompensationIntensive care medicineLiver transplantationCandidacyCirrhosisTransplantationIntervention (counseling)Liver diseasePortopulmonary hypertensionChronic liver diseaseLiver failureDiseaseInternal medicineNursing

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Patients with cirrhosis are frequently hospitalized with acute decompensation and organ system failure - a syndrome referred to as acute on chronic liver failure (ACLF). These patients often require critical care intervention and experience significant mortality; however, established diagnostic and prognostic criteria are lacking. Given this, it remains imperative for intensivists to develop an expertise in common ACLF complications and management. RECENT FINDINGS: Liver transplantation serves as the definitive management strategy in ACLF. Traditional organ allocation procedures are based on the Model for Endstage Liver Disease score, which may not correlate with ACLF severity and the associated need for urgent liver transplantation. Recent studies have suggested favorable postliver transplantation outcomes in ACLF patients with multiorgan failure, emphasizing the need for further studies to elucidate optimal timing and candidacy for liver transplantation. SUMMARY: Cirrhosis is a chronic and progressive condition leaving patients vulnerable to acute decompensation necessitating the need for critical care intervention. Prompt recognition and implementation of targeted supportive therapies, together with consideration of urgent liver transplantation, are essential to combat the high short-term mortality of ACLF patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.612
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.194
GPT teacher head0.479
Teacher spread0.286 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations8
Published2020
Admission routes1
Has abstractyes

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