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Renal Replacement Therapy in Special Settings: Extracorporeal Support Devices in Liver Failure

2011· review· en· W2148372270 on OpenAlexaff
Jorge Cerdá, Ashita Tolwani, R. T. Noel Gibney, Khajohn Tiranathanagul

Bibliographic record

VenueSeminars in Dialysis · 2011
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineExtracorporealRenal replacement therapyIntensive care medicineLiver failureInternal medicine

Abstract

fetched live from OpenAlex

Hepatorenal syndrome (HRS) type I is a unique form of acute kidney injury resulting from renal vasoconstriction in the setting of systemic and splanchnic arterial vasodilation in patients with end-stage liver disease. The only definitive treatment currently available is liver or liver-kidney transplantation. The goal of extracorporeal support (ECS) systems in HRS is to bridge eligible liver failure patients to transplantation or functional recovery by means of detoxification, assistance with biosynthesis of key metabolic products, and regulation of inflammation. ECS systems in liver disease can be divided into two broad categories: cell-based and noncell-based systems. While cell-based systems aim to provide functions similar to those of normal hepatocytes, noncell-based systems do not incorporate tissue; they provide detoxification utilizing membranes and adsorbents. There are no standard guidelines for the application of ECS systems. Published studies are too small and show considerable differences in primary indication, primary endpoint, and treatment protocols for "intervention" and "standard" treatment groups. This article reviews the available evidence regarding the optimal use of ECS devices in HRS, makes evidence-based practice recommendations, and delineates key questions for future studies.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.305
Teacher spread0.273 · 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 designOther design
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

Citations9
Published2011
Admission routes1
Has abstractyes

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