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Record W3122912123 · doi:10.3390/biomedicines9020106

Liraglutide-Induced Hepatotoxicity

2021· article· en· W3122912123 on OpenAlexaff
David Ergaz, Stephen Malnick, Ehud Melzer, Manuela G. Neuman

Bibliographic record

VenueBiomedicines · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLiraglutideMedicinePharmacovigilanceDrugPharmacologyIncretinExenatideSaxagliptinIntensive care medicineDiabetes mellitusType 2 diabetesEndocrinologySitagliptin

Abstract

fetched live from OpenAlex

was treated with the glucagon-like peptide 1 (GLP-1) agonist liraglutide as part of her weight-reduction program. Following this, she developed an idiosyncratic drug-related liver injury (IDILI). Advances in noninvasive techniques enabled this diagnosis to be established. By employing easily quantifiable methods based on serum biomarkers, we could explore a wide variety of endpoints in assessing personalized DILI. In addition, we can test endpoints that are associated with the drug's mechanism of action. Personalized medicine and therapeutic pharmacovigilance of incretin-based hypoglycemic agents are needed to ensure the safety of patients.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.287
Teacher spread0.258 · 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 designCase report
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

Citations21
Published2021
Admission routes1
Has abstractyes

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