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Record W4307840943 · doi:10.1080/14712598.2022.2142039

Hypersensitivity reactions to biologics in children

2022· review· en· W4307840943 on OpenAlexaff
Leticia de las Vecillas, Davide Caimmi, Ghislaine Annie Clarisse Isabwe, Ricardo Madrigal‐Burgaleta, Özge Soyer, Luciana Kase Tanno, Alessandra Vultaggio, Mattia Giovannini

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2022
Typereview
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineDesensitization (medicine)Intensive care medicinePopulationPremedicationMEDLINEMultidisciplinary approachPediatricsPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Hypersensitivity reactions (HSRs) have been observed with the use of biologics in children. The management of HSRs in children is mainly based on experiences from the adult population. Recently, data from different centers experienced in managing these reactions, including desensitization in children, have been published, allowing clinicians to have an appropriate global overview and compare results. AREAS COVERED: This review highlights the published data on hypersensitivity reactions to biologics in children and drug desensitization protocols adapted to the pediatric population. EXPERT OPINION: With regard to HSRs to biologics in children, few data are available. Compared with the adult population, there is a lack of knowledge in the endophenotypes, management and the standardization of protocols including premedication regimens in children. An international consensus is needed to provide clinicians with new insight on how to apply personalized management and to perform tailored desensitization protocols in pediatric populations. Various specialists including allergists, pediatricians, oncologists, hematologists, rheumatologists, and pharmacists, should build a multidisciplinary management team to keep pediatric patients on their best treatment options in the safest manner.

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 score0.998
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.282
GPT teacher head0.440
Teacher spread0.158 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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