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Record W2159548054 · doi:10.1186/1710-1492-4-4-144

Should Younger Siblings of Peanut-Allergic Children Be Assessed by an Allergist before Being Fed Peanut?

2008· article· en· W2159548054 on OpenAlexaffvenueabout
Joel Liem, Saiful Huq, Anita L. Kozyrskyj, Allan B. Becker

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2008
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of ManitobaWindsor Clinical Research
Fundersnot available
KeywordsPeanut allergyMedicineAllergyOdds ratioPediatricsConfidence intervalArachisPeanut butterFood allergyImmunologyBiologyFood scienceInternal medicineAgronomy

Abstract

fetched live from OpenAlex

: The objective of this study was to determine the risk of peanut allergy in siblings of peanut-allergic children. In 2005-2006, 560 households of children born in 1995 in the province of Manitoba, Canada, were surveyed. The index children (8-to 10-year-olds) were assessed by a pediatric allergist and had skin-prick testing and/or capRAST for peanut allergy. Surveys were completed by parents for siblings to determine the presence of peanut allergy. Of 560 surveys, 514 (92%) were completed. Twenty-nine (5.6%) index children were peanut allergic. Fifteen of 900 (1.7%) siblings had peanut allergy. Four of 47 (8.5%) were siblings of peanut-allergic children and 11 of 853 (1.3%) were siblings of non-peanut-allergic children. The risk of peanut allergy was markedly increased in siblings of a peanut-allergic child (odds ratio 6.72, 95% confidence interval 2.04-22.12). Siblings of peanut-allergic children are much more likely to be allergic to peanut. An allergy assessment by a qualified allergist should be routinely recommended before feeding peanut to these 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 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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.350
Teacher spread0.306 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations38
Published2008
Admission routes3
Has abstractyes

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