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Record W2995245446 · doi:10.1002/ncp.10459

Pathophysiology of Acute Liver Failure

2019· review· en· W2995245446 on OpenAlexaff
Victor Dong, Rahul Nanchal, Constantine Karvellas

Bibliographic record

VenueNutrition in Clinical Practice · 2019
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsUniversity of Alberta HospitalUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsMedicinePathophysiologySepsisAcetaminophenCoagulopathyLiver injuryHepatic encephalopathyEncephalopathyLiver diseaseSystemic inflammatory response syndromeImmunologyInternal medicineCirrhosisPharmacology

Abstract

fetched live from OpenAlex

Acute liver failure (ALF) is a rare syndrome resulting from an acute insult to the liver in patients without known underlying chronic liver disease. It is characterized by loss of synthetic function in the form of jaundice and coagulopathy and development of hepatic encephalopathy. Multiorgan failure (MOF) eventually develops, leading to death. Many different etiologies have been identified, with acetaminophen (APAP) overdose and viral hepatitis being the most common causes worldwide. The pathophysiology of ALF can be divided into cause-specific liver injury pathophysiologies and pathophysiology related to occurrence of secondary MOF. In terms of liver injury pathophysiology, APAP toxicity is the most well known. Secondary MOF is often a result of the initial massive proinflammatory response generating a systemic inflammatory response syndrome followed by a compensatory anti-inflammatory response leading to immune cell dysfunction and sepsis. As the liver is a tremendously important metabolic organ involved in energy metabolism, protein synthesis, fat metabolism, and glycemic control, multiple aspects of nutrition also need to be considered as part of the overall pathophysiology of ALF.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.445
GPT teacher head0.601
Teacher spread0.156 · 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
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

Citations148
Published2019
Admission routes1
Has abstractyes

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