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Record W3016798018 · doi:10.1542/peds.2019-2102

Peanut Allergy: New Advances and Ongoing Controversies

2020· review· en· W3016798018 on OpenAlexaff
Elissa M. Abrams, Edmond S. Chan, Scott H. Sicherer

Bibliographic record

VenuePEDIATRICS · 2020
Typereview
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsBC Children's HospitalUniversity of British ColumbiaUniversity of Manitoba
Fundersnot available
KeywordsPeanut allergyMedicineDesensitization (medicine)AllergySensitizationAnaphylaxisFood allergyImmunologyAllergenIntensive care medicineDermatologyInternal medicine

Abstract

fetched live from OpenAlex

Peanut allergy is one of the most common food allergies in children, with increasing prevalence over time. The dual-allergen exposure hypothesis now supports transcutaneous sensitization to peanut as a likely pathophysiologic mechanism for peanut allergy development. As a result, there is emerging evidence that early peanut introduction has a role in peanut allergy prevention. Current first-line diagnostic tests for peanut allergy have limited specificity, which may be enhanced with emerging tools such as component-resolved diagnostics. Although management of peanut allergy includes avoidance and carrying an epinephrine autoinjector, risk of fatal anaphylaxis is extremely low, and there is minimal risk related to cutaneous or inhalational exposure. Quality of life in children with peanut allergy requires significant focus. Moving forward, oral and epicutaneous immunotherapy are emerging and exciting tools that may have a role to play in desensitization to peanut.

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)0.002
metaresearch head score (Gemma)0.005
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: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.042
GPT teacher head0.348
Teacher spread0.307 · 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
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

Citations56
Published2020
Admission routes1
Has abstractyes

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