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Record W4391823476 · doi:10.1097/hep.0000000000000790

From past to present to future: Terlipressin and hepatorenal syndrome-acute kidney injury

2024· review· en· W4391823476 on OpenAlexaff
Andrew S. Allegretti, Kavish R. Patidar, Ann T., Giuseppe Cullaro

Bibliographic record

VenueHepatology · 2024
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsToronto Liver CentreUniversity Health Network
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsTerlipressinHepatorenal syndromeMedicineAcute kidney injuryIntensive care medicineCirrhosisIntravascular volume statusKidney diseasePopulationInternal medicine

Abstract

fetched live from OpenAlex

Hepatorenal syndrome (HRS) is a rare and highly morbid form of kidney injury unique to patients with decompensated cirrhosis. HRS is a physiologic consequence of portal hypertension, leading to a functional kidney injury that can be reversed by restoring effective circulating volume and renal perfusion. While liver transplantation is the only definitive "cure" for HRS, medical management with vasoconstrictors and i.v. albumin is a cornerstone of supportive care. Terlipressin, a V1a receptor agonist that acts on the splanchnic circulation, has been used for many years outside the United States for the treatment of HRS. However, its recent Food and Drug Administration approval has generated new interest in this population, as a new base of prescribers now work to incorporate the drug into clinical practice. In this article, we review HRS pathophysiology and diagnostic criteria, the clinical use of terlipressin and alternative therapies, and identify areas of future research in the space of HRS and kidney injury in cirrhosis.

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 score0.661
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.022
GPT teacher head0.346
Teacher spread0.324 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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