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Record W2973459140 · doi:10.1159/000501518

Antibiotic Allergy in Children: More than Just a Label

2019· review· en· W2973459140 on OpenAlexaff
Elissa M. Abrams, Elena Netchiporouk, Barbara Miedzybrodzki, Moshe Ben‐Shoshan

Bibliographic record

VenueInternational Archives of Allergy and Immunology · 2019
Typereview
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsMontreal Children's HospitalMcGill UniversityUniversity of Manitoba
Fundersnot available
KeywordsAllergyAntibioticsMedicinePenicillinPenicillin allergyDrug allergyIntensive care medicineNarrative reviewCephalosporinAdverse effectProvocation testAdverse drug reactionPediatricsDrugImmunologyAlternative medicineInternal medicinePharmacologyPathologyMicrobiology

Abstract

fetched live from OpenAlex

Within the broad category of adverse drug reactions in children, there has been a recent focus specifically on the evaluation of children with antibiotic allergy, in particular, beta-lactam allergy. The potential consequences of being labeled beta-lactam allergy are increasingly recognized. Appropriate evaluation of children with suspected reactions to antibiotics is essential as it is increasingly being recognized that the label of "penicillin allergy" is associated with adverse health and economic outcomes. This review will focus on the 3 main classes of antibiotics reported to cause allergic reactions in children: beta lactams (penicillin derivatives and cephalosporins), macrolides, and sulfonamides. This article is a narrative review of the prevalence, diagnosis, and management of different types of antibiotic allergies in children. Our review reveals that antibiotic allergy is often overreported and not appropriately diagnosed in the pediatric age groups. There is a recent shift in the diagnostic paradigm from the use of skin tests and if negative challenges to the use of challenge only in the pediatric age group. Larger studies to establish the usefulness and safety of this new approach as well as updated guidelines are needed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.035
GPT teacher head0.329
Teacher spread0.295 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations29
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Archives of Allergy and ImmunologySame topicDrug-Induced Adverse ReactionsFrench-language works237,207