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Record W4386457522 · doi:10.1089/derm.2022.0032

Characteristics of DRESS Syndrome in the Elderly: A Comparative Study of 55 Patients

2023· article· en· W4386457522 on OpenAlexvenueno aff
E. Bahloul, Khaoula Trimeche, Khadija Sellami, Fatma Hammami, Faten Hayder, R. Chaabouni, M. Amouri, A. Masmoudi, Madiha Mseddi, Sonia Boudeya, H. Turki

Bibliographic record

VenueDermatitis · 2023
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCulpritAllopurinolEosinophiliaSepsisDermatologyInternal medicineEpidemiologyRashRetrospective cohort studySurgery

Abstract

fetched live from OpenAlex

Abstract: Background: Drug reaction with eosinophilia and systemic symptoms (DRESS) is a rare drug reaction characterized by a skin rash, eosinophilia, and organ involvement. Objective: Our purpose is to focus on the clinical and epidemiological characteristics of DRESS in the elderly and to identify the incriminated drugs. Methods: This is a retrospective study including patients, hospitalized for DRESS with a RegiSCAR ≥4. The population was divided into 2 groups according to age: 65 years or older (G1) and <65 years (G2). The statistical study was performed using the comparative and multivariate analysis. Results: We included 55 patients (30.9% G1 and 69.1% G2). Skin manifestations were comparable in both groups. Lymphadenopathy was less common in G1 with a statistically significant difference ( P = 0.012). Renal impairment was more frequent in the elderly with a statistically significant result ( P = 0.005). DRESS in the elderly group was significantly associated with the occurrence of sepsis ( P = 0.008). Allopurinol was the most common culprit associated with DRESS in G1 ( P = 0.001). Relapses and recurrences were comparable in both groups ( P = 0.71). Conclusions: DRESS in the elderly is associated with a high risk of complications, mainly kidney involvement and sepsis. Allopurinol is the most incriminated drug.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.313
Teacher spread0.271 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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