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Record W3203475916 · doi:10.1111/liv.15069

Sequential changes in urinary biomarker levels in patients with cirrhosis and severe hepatorenal syndrome

2021· article· en· W3203475916 on OpenAlexfundno aff
Cristina Solé, Ann T., Elsa Solà, Marta Carol, Núria Fabrellas, Adrià Juanola, Laura Napoleone, Jordi Gratacós‐Ginès, Octavi Bassegoda, Marta Cervera, Martina Pérez, Ana Belén Rubio, Emma Avitabile, Manuel Morales‐Ruiz, Isabel Graupera, Elisa Pose, Patrick S. Kamath, Pere Ginès

Bibliographic record

VenueLiver International · 2021
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsnot available
FundersInstituto de Salud Carlos IIICanadian Liver FoundationCanadian Association for the Study of the Liver
KeywordsHepatorenal syndromeMedicineAcute kidney injuryCirrhosisInternal medicineLiver transplantationGastroenterologyAcute tubular necrosisUrinary systemBiomarkerRenal replacement therapyCreatinineTransplantationKidney

Abstract

fetched live from OpenAlex

Whether tubular injury develops in patients with acute kidney injury owing to hepatorenal syndrome (AKI-HRS) is controversial. We performed repeated measurements of biomarkers of tubular injury during a 14-day period in 60 patients with cirrhosis and AKI (34 with AKI-HRS meeting the classical definition of type 1 HRS and 26 with AKI owing to acute tubular necrosis, AKI-ATN). Nineteen of 34 patients had resolution of AKI-HRS, while the remainder had persistent AKI-HRS. The persistence of AKI-HRS was associated with remarkably high short-term mortality. There were no significant differences in urinary NGAL or IL-18 between patients with resolution vs those with persistent AKI-HRS throughout the 14-day period. By contrast, biomarker levels were significantly lower in AKI-HRS, even if persistent, compared to AKI-ATN. These findings are highly suggestive of lack of significant tubular injury in AKI-HRS and could be of value in the clinical decision between combined liver-kidney or liver transplantation alone in patients with cirrhosis and AKI candidates to transplantation.

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 score0.004
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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