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Record W2737414819 · doi:10.1177/1756285617722352

Liver injury and glatiramer acetate, an uncommon association: case report and literature review

2017· review· en· W2737414819 on OpenAlexfundno aff
Núria Solà‐Valls, Elisa Pose, Yolanda Blanco, María Sepúlveda, Sara Llufriú, Pere Ginès, Albert Saiz

Bibliographic record

VenueTherapeutic Advances in Neurological Disorders · 2017
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsnot available
FundersAlberta Innovates - Health SolutionsBiogenSanofi
KeywordsMedicineGlatiramer acetateAutoimmune hepatitisDiscontinuationLiver injuryMagnetic resonance imagingLiver functionLesionMultiple sclerosisLiver biopsyPast medical historyHepatitisRadiologyGastroenterologyInternal medicineSurgeryBiopsyImmunology

Abstract

fetched live from OpenAlex

We report the case of a 65-year-old woman who presented with a 1-month history of progressive paraparesia associated with a thoracic lesion with irregular ring-like gadolinium enhancement. Biopsy of the lesion confirmed the demyelinating origin and brain magnetic resonance imaging showed additional lesions demonstrative of dissemination in space. Immunomodulatory therapy with glatiramer acetate (GA) was started after having a second relapse 2 months later. Shortly after initiation, the patient developed acute hepatitis. Liver function tests returned to normal values 5 months after discontinuation and the patient was diagnosed with drug-induced liver injury (DILI) associated with GA. A literature review identified 11 previous cases of GA-related liver injury associated with two specific mechanisms: DILI (seven cases) and autoimmune hepatitis (four cases). Despite the fact that GA hepatic toxicity is uncommon and laboratory monitoring is not required during GA therapy, it should be considered at least in some special conditions such as comorbidities and previous history of DILI associated with other drugs.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.003
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 head0.175
GPT teacher head0.500
Teacher spread0.324 · 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 designOther design
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

Citations12
Published2017
Admission routes1
Has abstractyes

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