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Record W2777947423 · doi:10.1055/s-0037-1608772

Management of Cerebral Edema in Acute Liver Failure

2017· review· en· W2777947423 on OpenAlexaff
Beverley Kok, Constantine Karvellas

Bibliographic record

VenueSeminars in Respiratory and Critical Care Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineCerebral edemaLiver failureEdemaBrain edemaIntensive care medicineAnesthesiaSurgery

Abstract

fetched live from OpenAlex

Advances in medical care of the acute liver failure patient have led to a significant reduction in mortality related to the condition. Nevertheless, cerebral edema and ensuing brain herniation remains one of the top causes of demise in acute liver failure. Controversy remains regarding the utility of invasive intracranial pressure monitoring as well as usage of novel treatment modalities including therapeutic hypothermia. This review provides a brief summary into the pathophysiology and risk factors for developing cerebral edema in the context of acute liver failure; this review particularly provides a practical focus on general management of the patient with established cerebral edema as well as specific intracranial pressure-lowering strategies.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.062
GPT teacher head0.396
Teacher spread0.333 · 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

Citations29
Published2017
Admission routes1
Has abstractyes

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