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Record W4388448741 · doi:10.1016/j.jcrc.2023.154456

What every Intensivist should know about ... Ammonia in liver failure

2023· review· en· W4388448741 on OpenAlexaff
Tiago L. Duarte, Pedro Fidalgo, Constantine Karvellas, Filipe S. Cardoso

Bibliographic record

VenueJournal of Critical Care · 2023
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsIntensivistMedicineIntensive care medicineLiver failureInternal medicineIntensive care

Abstract

fetched live from OpenAlex

PURPOSE: Acute liver failure (ALF) or acute-on-chronic liver failure (ACLF) patients have high short-term mortality and morbidity. In the context of liver failure, increased serum ammonia is associated with worse neurological outcomes, including high-grade hepatic encephalopathy (HE), cerebral edema, and intracranial hypertension. Besides its neurotoxicity, hyperammonemia may contribute to immune dysfunction and the risk of infection, a frequent trigger for multi-organ failure in these patients. MATERIAL AND METHODS: We performed a literature-based narrative review. Publications available in PubMed® up to June 2023 were considered. RESULTS: In the ICU management of liver failure patients, serum ammonia may play an important role. Accordingly, in this review, we focus on recent insights about ammonia metabolism, serum ammonia measurement strategies, hyperammonemia prognostic value, and ammonia-targeted therapeutic strategies. CONCLUSIONS: Serum ammonia may have prognostic value in liver failure. Effective ammonia targeted therapeutic strategies are available, such as laxatives, rifaximin, L-ornithine-l-aspartate, and continuous renal replacement therapy.

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 score0.596
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.096
GPT teacher head0.415
Teacher spread0.319 · 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

Citations14
Published2023
Admission routes1
Has abstractyes

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