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Record W2154081213 · doi:10.1186/1710-1492-3-4-114

Nonsteroidal Anti-Inflammatory Drug Hypersensitivity in Preschool Children

2007· article· en· W2154081213 on OpenAlexvenueno aff
Mona Kidon, Liew Woei Kang, Chiang Wen Chin, Lim Siok Hoon, Van Bever Hugo

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2007
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAntipyreticIbuprofenBronchoconstrictionAcetaminophenAsthmaDrugDiseaseCyclooxygenaseAnalgesicNonsteroidalAdverse effectInternal medicineImmunologyPharmacologyEnzyme

Abstract

fetched live from OpenAlex

: Although extensively studied in adults, nonsteroidal anti-inflammatory drug (NSAID) hypersensitivity in children, especially in young children, remains poorly defined. Pediatricians, prescribing antipyretics for children, rarely encounter significant problems, but the few epidemiologic studies performed show conflicting results. Although it is clear that some patients with acetylsalicylic acid (ASA)-sensitive asthma have their clinical onset of disease in childhood and bronchoconstriction after ASA challenge is seen in 0 to 22% of asthmatic children so challenged, ibuprofen at antipyretic doses may cause acute respiratory problems only in a very small number of mild to moderate asthmatics. The recently elucidated mechanism of action of acetaminophen may explain some occurrences of adverse reactions in patients with cross-reactive NSAID hypersensitivity on the basis of its inhibitory activity on the newly described enzyme, cyclooxygenase (COX)-3. This nonspecific sensitivity to inhibition of COX is most likely genetically determined and shows a remarkable association with atopic disease even in the very young age group and possibly an increased predilection in specific ethnic groups. This review summarizes state-of-the-art published data on NSAID hypersensitivity in preschool children.

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)0.001
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 score0.403
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.011
GPT teacher head0.296
Teacher spread0.285 · 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

Citations30
Published2007
Admission routes1
Has abstractyes

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