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Record W1979447809 · doi:10.14740/jmc.v5i7.1811

Drug Rash With Eosinophilia and Systemic Symptoms Syndrome Induced by Allopurinol

2014· article· en· W1979447809 on OpenAlexvenueno aff
Mukaddes Kavala, Ayşe Serap Karadağ, Filiz Topaloğlu Demir, İlkin Zindancı, Zafer Türkoğlu, Berkant Oman, Ebru Zemheri, Emin Özlü

Bibliographic record

VenueJournal of Medical Cases · 2014
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEosinophiliaRashAllopurinolMaculopapular rashDermatologyAdverse drug reactionDrugInternal medicinePharmacology

Abstract

fetched live from OpenAlex

Drug rash with eosinophilia and systemic symptoms (DRESS) syndrome is an adverse drug reaction caused by an apparent group of drugs which can cause 10-20% mortality. It is characterized by a latency period ranging from 3 weeks to 3 months after the introduction of the offending drug. The syndrome is defined by the presence of fever, rash, eosinophilia, atypical lymphocytes and multiorgan involvement. We present a 39-year-old woman who developed fever, nausea, a pruritic erythematous maculopapular rash and facial edema during her sixth week of the treatment with allopurinol as a case of DRESS syndrome. Diagnosis was confirmed by the drug rash, eosinophilia and systemic involvement including adenopathy, toxic hepatitis and pericardial effusion. Allopurinol was discontinued and intravenous prednisolone 60 mg/day was started. The patient’s clinical appearance and eosinophilia improved within first 2 days. Awareness of hematologic abnormalities and systemic involvement along with drug rush, by physicians is critical for early diagnosis of this life-threatening syndrome. J Med Cases. 2014;5(7):420-422 doi: http://dx.doi.org/10.14740/jmc1811w

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.264
Teacher spread0.252 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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