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Record W2020691793 · doi:10.14740/jmc.v6i1.2009

Hepatotoxicity and Anti-Nuclear Antibody Positivity During Methimazole Therapy for Thyrotoxicosis: A Case Report

2015· article· en· W2020691793 on OpenAlexvenueno aff
Ali Gökyer, Mehmet Çelik, Hüseyin Çelik, Semra Aytürk, Sibel Güldiken, Armağan Tuğrul, Ayten Üstündağ, Ahmet Tezel

Bibliographic record

VenueJournal of Medical Cases · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsnot available
Fundersnot available
KeywordsMethimazoleMedicineThyroidGastroenterologyInternal medicineAntibodyEndocrine systemAdverse effectAutoimmune hepatitisDrugAntithyroid drugsHepatitisGraves' diseasePharmacologyImmunologyHormone

Abstract

fetched live from OpenAlex

Thyrotoxicosis is one of the most frequent endocrine disorders. Methimazole is the anti-thyroid drug for the treatment of hyperthyrodism. Methimazole is rarely associated with hepatic toxicity. We reported a case of 56-year-old woman who developed hepatotoxicity and anti-nuclear antibody positivity during methimazole treatment. Increased liver enzyme levels appeared after 1 month of the methimazole treatment. Viral hepatitis markers were negative. The radiological examinations were normal. According to the CIOMS/RUCAM scale, the score was 11 and the causal relationship of the hepatic adverse reaction by methimazole was highly probable. Methimazole-induced hepatotoxicity was considered and methimazole treatment was cancelled. The laboratory parameters returned to normal after 15 days. The mechanism of hepatotoxicity due to methimazole treatment was not fully understood. It has been hypothesized that there is a genetic predisposition, associated with a dose-dependent immune reaction. In conclusion, physicians should be aware the risk of hepatotoxicity related with methimazole treatment. J Med Cases. 2015;6(1):30-32 doi: http://dx.doi.org/10.14740/jmc2009w

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.163
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.191
GPT teacher head0.478
Teacher spread0.287 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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