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Record W2978800811 · doi:10.14740/gr1212

Acute Liver Injury in a Patient Treated With Rosuvastatin: A Rare Adverse Effect

2019· article· en· W2978800811 on OpenAlexvenueno aff
Jamil Shah, Vivek Lingiah, Nikolaos Pyrsopoulos, Mark Galan

Bibliographic record

VenueGastroenterology Research · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRosuvastatinLiver injuryAutoimmune hepatitisIncidence (geometry)Internal medicinePopulationHepatitisGastroenterologyLiver biopsyAsymptomaticAdverse effectDrugBiopsyPharmacology

Abstract

fetched live from OpenAlex

Drug-induced liver injury (DILI) is among the challenging liver conditions encountered by clinicians today. It has a low incidence in the general population with an approximated annual incidence of 10 - 15 cases per 10,000 - 100,000 persons who have taken prescription medications. Nevertheless, DILI remains the most frequent cause of acute liver injury in the United States. Rosuvastatin is a commonly prescribed medication that, similar to other statins, is associated with serum aminotransferase elevations that are mild, asymptomatic and usually self-limited. Here, we report a case of a man who developed acute liver injury after taking rosuvastatin for hypercholesterolemia treatment. Moreover, DILI with autoimmune features represents a key subgroup of hepatotoxicity attributable to medication exposure. Similar to idiopathic autoimmune hepatitis, circulating autoantibodies and a hypergammaglobulinemia are often present in the serum of such individuals. However, such findings are not invariable. In the case reported here, these laboratory features were absent, but a liver biopsy demonstrated interface hepatitis with a prominent plasma cell infiltrate, histologic components consistent with an immune-mediated drug reaction. After withdrawal of the offending medication did not result in complete resolution, corticosteroid therapy was administered with a subsequent clinical response, confirming the diagnosis.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.075
GPT teacher head0.425
Teacher spread0.350 · 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.

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

Citations16
Published2019
Admission routes1
Has abstractyes

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