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Enregistrement W3106664023 · doi:10.1542/peds.2020-023861ll

Clinical Factors Associated With Peanut Allergy in a High-Risk Infant Cohort

2020· article· en· W3106664023 sur OpenAlexaffabout
Amarjot Padda, Elinor Simons

Notice bibliographique

RevuePEDIATRICS · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueRespiratory viral infections research
Établissements canadiensResearch Manitoba
Organismes subventionnairesnon disponible
Mots-clésMedicineDisclaimerPublicationPediatricsFamily medicineMEDLINELibrary scienceAdvertising

Résumé

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AAP Policy SupplementsSupplements Publish Supplement MultimediaVideo Abstracts Pediatrics On Call Podcast Subscribe Alerts Careers We will not be accepting article comments until November 8, 2021, while our site undergoes major changes. We apologize for the inconvenience. For questions, contact the editorial office. Food Allergy Clinical Factors Associated With Peanut Allergy in a High-Risk Infant Cohort Amarjot Padda and Elinor Simons Pediatrics December 2020, 146 (Supplement 4) S344; DOI: https://doi.org/10.1542/peds.2020-023861LL Amarjot Padda Winnipeg, ManitobaFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteElinor Simons Winnipeg, ManitobaFind this author on Google ScholarFind this author on PubMedSearch for this author on this site ArticleInfo & MetricsComments Download PDF SH Sicherer, RA Wood, TT Perry. Allergy. 2019;74(11):2199–2211PURPOSE OF THE STUDY:This study examined factors associated with the development of peanut allergy in high risk infants.STUDY POPULATION:The study included 511 infants aged 3–15 months from the prospective, observational Consortium for Food Allergy Research (CoFAR2) study, who were at high risk of peanut allergy because of moderate-to-severe atopic dermatitis (39.5%), egg or milk allergy (17.8%) or both (42.7%). Infants with known peanut allergy or peanut-specific IgE >5 kU/L at the time of enrollment were excluded.METHODS:Participants were assessed for peanut allergy at enrollment, 6 months, 12 months, and annually thereafter based on history of reactions, skin prick testing (SPT), peanut IgE results, and oral challenges if clinically indicated. A prediction model was developed by stepwise multiple logistic regression and validated with a subset of the data.RESULTS:Among the 511 infants (67.5% male, 82% with moderate-to-severe atopic dermatitis, median age 9.9 months and median length of follow-up 7.3 years), 40.1% developed peanut allergy and 10.6% outgrew their peanut allergy. Factors associated with developing peanut allergy (P < .05) included: moderate-severe atopic dermatitis; larger egg and peanut SPT; greater egg, milk and peanut IgE levels; greater peanut component (Ara h1, h2 and h3) levels; greater peanut IgG and IgG4; peanut consumption >2 times per week in pregnancy; younger age; non-white race; lack of breastfeeding; and increased peanut consumption during lactation. The final model included age at enrollment, peanut-specific IgE level, peanut Ara h2, and breastfeeding status and predicted 79.4% of peanut allergy in the development data set and 74.8% of peanut allergy in the validation data set (sensitivity 66.1% and specificity 80.6%).CONCLUSIONS:Among infants at high risk of peanut allergy because of moderate-severe atopic dermatitis and/or egg or milk allergy, peanut allergy development may be predicted by younger age, greater peanut IgE and Ara h2 levels, and lack of breastfeeding.REVIEWER COMMENTS:In addition to infants with moderate-severe atopic dermatitis and egg allergy previously reported to have a high risk of peanut allergy, this cohort also included infants with milk allergy. These high-risk children, without peanut allergy at study entry, had a higher proportion of peanut allergy development and lower proportion of peanut allergy outgrowing than typically reported. The model requires further validation in high-risk infants and may not be generalizable to low-risk infants. Infants referred at a younger age, lacking breastfeeding, and with higher levels of sensitization to peanut were identified by the model as having the highest likelihood of peanut allergy development and may benefit from even closer monitoring than typical for this high-risk group. However, strategies for prevention of peanut allergy, such as early dietary peanut introduction, should be applied to all high-risk infants.Copyright © 2020 by the American Academy of Pediatrics PreviousNext Back to top Advertising Disclaimer » In this issue Pediatrics Vol. 146, Issue Supplement 4 1 Dec 2020 Table of ContentsIndex by author View this article with LENS PreviousNext Email Article Thank you for your interest in spreading the word on American Academy of Pediatrics.NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Clinical Factors Associated With Peanut Allergy in a High-Risk Infant Cohort Message Subject (Your Name) has sent you a message from American Academy of Pediatrics Message Body (Your Name) thought you would like to see the American Academy of Pediatrics web site. Your Personal Message CAPTCHAThis question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Request Permissions Article Alerts Log in You will be redirected to aap.org to login or to create your account. Or Sign In to Email Alerts with your Email Address Email * Citation Tools Clinical Factors Associated With Peanut Allergy in a High-Risk Infant Cohort Amarjot Padda, Elinor Simons Pediatrics Dec 2020, 146 (Supplement 4) S344; DOI: 10.1542/peds.2020-023861LL Citation Manager Formats BibTeXBookendsEasyBibEndNote (tagged)EndNote 8 (xml)MedlarsMendeleyPapersRefWorks TaggedRef ManagerRISZotero Share Clinical Factors Associated With Peanut Allergy in a High-Risk Infant Cohort Amarjot Padda, Elinor Simons Pediatrics Dec 2020, 146 (Supplement 4) S344; DOI: 10.1542/peds.2020-023861LL Share This Article: Copy Print Download PDF Insight Alerts Table of Contents Jump to section ArticlePURPOSE OF THE STUDY:STUDY POPULATION:METHODS:RESULTS:CONCLUSIONS:REVIEWER COMMENTS:Info & MetricsComments Related ArticlesNo related articles found.Google Scholar Cited By...No citing articles found.Google Scholar More in this TOC Section Oral Immunotherapy for Multiple Foods in a Pediatric Allergy Clinic Setting Estimated Risk Reduction to Packaged Food Reactions by Epicutaneous Immunotherapy (EPIT) for Peanut Allergy Show more Food Allergy Similar Articles Journal Info Editorial Board Editorial Policies Overview Licensing Information Authors/Reviewers Author Guidelines Submit My Manuscript Open Access Reviewer Guidelines Librarians Institutional Subscriptions Usage Stats Support Contact Us Subscribe Resources Media Kit About International Access Terms of Use Privacy Statement FAQ AAP.org shopAAP Follow American Academy of Pediatrics on Instagram Visit American Academy of Pediatrics on Facebook Follow American Academy of Pediatrics on Twitter Follow American Academy of Pediatrics on Youtube RSS © 2021 American Academy of Pediatrics

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,030

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0090,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,077
Tête enseignante GPT0,365
Écart entre enseignants0,288 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission2
Résumé présentoui

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