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Record W2802722567 · doi:10.1002/hep.30056

Acute‐on‐Chronic Liver Failure: Getting Ready for Prime Time?

2018· review· en· W2802722567 on OpenAlexaff
Jasmohan S. Bajaj, Richard Moreau, Patrick S. Kamath, Hugo E. Vargas, Vicente Arroyo, K. Rajender Reddy, Gyöngyi Szabó, Puneeta Tandon, Jody C. Olson, Constantine Karvellas, Thierry Gustot, Jennifer C. Lai, Florence Wong

Bibliographic record

VenueHepatology · 2018
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of TorontoUniversity of Alberta
FundersGrifolsGilead Sciences
KeywordsMedicineIntensive care medicineDiseasePopulationLiver diseaseKidney diseaseLiver failurePathologySurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Acute on chronic liver failure (ACLF) is the culmination of chronic liver disease and extrahepatic organ failures, which is associated with a high short-term mortality and immense health care expenditure. There are varying definitions for organ failures and ACLF in Europe, North America, and Asia. These differing definitions need to be reconciled to enhance progress in the field. The pathogenesis of ACLF is multifactorial and related to interactions between the immunoinflammatory system, microbiota, and the various precipitating factors. Individual organ failures related to the kidney, brain, lungs, and circulation have cumulative adverse effects on mortality and are often complicated or precipitated by infections. Strategies to prevent and rapidly treat these organ failures are paramount in improving survival. With the aging population and paucity of organs for liver transplant, the prognosis of ACLF patients is poor, highlighting the need for novel therapeutic strategies. The role of liver transplant in ACLF is evolving and needs further investigation across large consortia. A role for early palliative care and management of frailty as approaches to alleviate disease burden and improve patient-reported outcomes is being increasingly recognized. CONCLUSION: ACLF is a clinically relevant syndrome that is epidemic worldwide and requires a dedicated multinational approach focused on prognostication and management; investigations are underway worldwide to prepare ACLF for prime time. (Hepatology 2018; 00:000-000).

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score1.000

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.060
GPT teacher head0.362
Teacher spread0.302 · 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.

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

Citations102
Published2018
Admission routes1
Has abstractyes

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