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Record W4403175432 · doi:10.1055/a-2435-2091

Patient-Reported Outcomes in Metabolic Dysfunction-Associated Steatotic Liver Disease

2024· review· en· W4403175432 on OpenAlexaff
Aurora Barberá, Trenton M. White, Anish Arora, Linda Henry, Jeffrey V. Lazarus, Zobair M. Younossi

Bibliographic record

VenueSeminars in Liver Disease · 2024
Typereview
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSteatohepatitisMedicineDyslipidemiaCirrhosisMetabolic syndromeQuality of life (healthcare)Context (archaeology)DiseaseIntensive care medicineLiver diseaseDiabetes mellitusType 2 diabetesFatty liverObesityInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease worldwide and can progress to serious complications, including metabolic dysfunction-associated steatohepatitis (MASH), cirrhosis, end-stage liver disease, and hepatocellular carcinoma. Predisposing risk factors for MASH include obesity, type 2 diabetes, dyslipidemia, and metabolic syndrome. Patients with MASH often experience significant impairments in their health-related quality of life and other patient-reported outcomes (PROs), particularly in physical functioning domains, fatigue, and vitality. Incorporating PROs offers valuable insights into patients' perspectives on their symptoms, treatment efficacy, and overall well-being, thereby guiding more holistic and patient-centered care strategies. This review aims to investigate the utilization of patient-reported outcome measures (PROMs) in the context of MASLD and MASH care, identify which PROMs are employed, and summarize the outcomes reported.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.311
Teacher spread0.282 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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