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

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

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

Bibliographic record

VenuePEDIATRICS · 2020
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsResearch Manitoba
Fundersnot available
KeywordsMedicineDisclaimerPublicationPediatricsFamily medicineMEDLINELibrary scienceAdvertising

Abstract

fetched live from OpenAlex

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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

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.077
GPT teacher head0.365
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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