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Record W2322133337 · doi:10.1097/der.0b013e31823d1aae

Prevalence of Cutaneous Adverse Drug Reactions in Southwest China: An 11-Year Retrospective Survey on In-patients of a Dermatology Ward

2012· article· en· W2322133337 on OpenAlexvenueno aff
Hua Zhong, Ziyuan Zhou, Huan Wang, Jun Niu, WenChieh Chen, Zhiqiang Song, Fei Hao

Bibliographic record

VenueDermatitis · 2012
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChinaDrugDermatologyTraditional medicineDrug reactionAdverse effectPharmacologyArchaeologyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: An update of the information about the prevailing trend of cutaneous adverse drug reactions (CADRs) is important for clinicians. OBJECTIVE: The objective of the study was to survey the prevalence of CADRs in Southwest China over the past 11 years. METHODS: The clinical and laboratory data of all inpatients admitted with a diagnosis of CADRs to the dermatology ward of Southwest Hospital during the past 11 years were retrospectively investigated. RESULTS: In the 547 recruited patients, the most common clinical pattern was maculopapular eruptions (n = 277), followed by fixed drug eruptions (n = 84) and acute urticaria (n = 44). In 206 cases with single medication intake, the 3 most common culprit drugs were acetaminophen (n = 44), penicillins (n = 44), and cephalosporins (n = 30). The frequency of urticaria in the elderly (≥60 years old) (n = 117) was significantly lower than that in younger patients (<60 years old) (n = 430) (P = 0.046), whereas erythema multiforme was much more common in the elderly (P = 0.038). As compared with younger patients, allopurinol was the most common culprit drug in the elderly. CONCLUSIONS: In contrast to previous studies, our study showed that the prevalence profiles of CADRs in the elderly are quite different from those in younger population. Acetaminophen was the most common culprit drug for total CADRs, which should be alerted as an important public health problem.

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 score0.005
Threshold uncertainty score1.000

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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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

Citations18
Published2012
Admission routes1
Has abstractyes

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