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Record W2211681973 · doi:10.1093/ndt/gfv190.34

SP222REDUCTION IN ACUTE KIDNEY INJURY (AKI) STAGE IS A STRONG PREDICTOR OF SURVIVAL IN PATIENTS WITH HEPATORENAL SYNDROME TYPE-1 (HRS-1) TREATED WITH TERLIPRESSIN PLUS ALBUMIN OR ALBUMIN ALONE

2015· article· en· W2211681973 on OpenAlexaff
Florence Wong, Thomas Boyer, Arun J. Sanyal, Lakhmir S. Chawla, S. Chris Pappas, Shannon Escalante, Jim Potenziano, Khurram Jamil

Bibliographic record

VenueNephrology Dialysis Transplantation · 2015
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHepatorenal syndromeTerlipressinMedicineAcute kidney injuryAlbuminKidney diseaseInternal medicineGastroenterologyCardiorenal syndromeIntensive care medicineCirrhosis

Abstract

fetched live from OpenAlex

Introduction and Aims: HRS-1 is a form of acute kidney injury (AKI) that occurs in patients with end-stage liver disease (ESLD). It is a serious complication that is often fatal if not treated. For AKI in general, it is well established that an increase in AKI stage (using the AKI Network classification), is associated with decreased survival. Conversely, data examining improvement of renal function (i.e. a decrease in AKI stage) and its correlation with survival is limited, particularly in HRS-1. The purpose of this study was to evaluate the impact of changes in AKI stage on survival in patients with HRS-1 in the REVERSE study, the largest, prospective, randomized trial of terlipressin plus albumin vs. albumin alone in cirrhotic patients with HRS-1. Methods: 196 subjects with well documented HRS-1, based on International Ascites Club (IAC) criteria were enrolled; baseline AKI stage was determined using serial serum creatinine (SCr) values collected as part of the pre-enrollment assessment. Subjects were randomized to receive terlipressin (1 mg IV every 6 hours) or placebo, plus albumin. Change in AKI stage from baseline to end of study treatment (EOT) and overall survival at Day 90 were evaluated.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.249
Teacher spread0.237 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2015
Admission routes1
Has abstractyes

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