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Record W4414320778 · doi:10.29173/hsi496

Albumin as a Marker of Ascites: The Role of Proteomics in Uncovering Novel Diagnostic Biomarkers

2025· article· en· W4414320778 on OpenAlex
J T Momoh, Ifeanyi Kennedy Nmecha

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueHealth Science Inquiry · 2025
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAcute kidney injuryAlbuminLipocalinCirrhosisProteomicsDiagnostic biomarkerKidneyBiomarker

Abstract

fetched live from OpenAlex

Liver cirrhosis is a major global health concern, often progressing to ascites, which worsens prognosis and increases healthcare costs. Current diagnostic approaches rely on albumin-based markers like the serum-ascites albumin gradient, but these have limitations. Emerging biomarkers such as kidney injury molecule-1 and neutrophil gelatinase-associated lipocalin show promise in identifying acute kidney injury in cirrhotic patients. Integrating multi-omics approaches may improve early detection and management of cirrhosis-related complications. Using kidney injury molecule-1 and neutrophil gelatinase-associated lipocalin alongside traditional markers could enhance risk stratification and patient outcomes.

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.

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)0.001
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 score0.011
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.349
Teacher spread0.325 · 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