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Record W2586422039 · doi:10.1186/s13223-017-0179-8

Prediction of clinical peanut allergy status among children in Hamilton, Ontario using chart review data collected during 2012–2015

2017· article· en· W2586422039 on OpenAlexafffundvenueabout
Elizabeth Simms, Gary Foster, Katherine Arias, Mark Larché, Tosha Freitag, Tina D. Walker, Susanna Goncharova, Andrea Marrin, Andreas Freitag, Manel Jordana, Susan Waserman

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2017
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsSt. Joseph's HospitalMcMaster UniversityAstraZeneca (Canada)St. Joseph’s Healthcare Hamilton
FundersAllerGen
KeywordsPeanut allergyChartAllergyMedicineStatisticsEnvironmental healthMathematicsFood allergyImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Peanut sensitization does not necessarily indicate clinical peanut allergy, and uncertainty as to whether or not there is true peanut allergy can lead to increased anxiety and decreased quality of life for patients and their families. The gold standard for diagnosing clinical peanut allergy is the oral food challenge, but this method is time-consuming and can cause severe allergic reactions. It would therefore be beneficial to develop a tool for predicting clinical peanut allergy in peanut-sensitized individuals whose peanut allergy status is unknown so as to better determine who requires an oral food challenge for diagnosis. METHODS: Two separate studies were conducted. In Study 1, we recruited 100 participants from the allergy clinic at McMaster University and community allergy outpatient clinics in the greater Hamilton area. We examined 18 different variables from participants and used univariate and multivariable logistic regression analysis to determine how well these variables, singly and in combination, were able to predict clinical peanut allergy status. In Study 2, we conducted a retrospective chart review of a second cohort of 194 participants to investigate the reproducibility of our findings. This was a matched case-control study where 97 peanut-allergic participants were gender- and age-matched to 97 non-allergic control participants. RESULTS: Peanut skin prick test wheal size was the best predictor of clinical peanut allergy in both study cohorts. For every 1 mm increase in wheal size, the odds ratio of an individual having clinical peanut allergy was 2.36 in our first cohort and 4.85 in our second cohort. No other variable approached the predictive power of wheal size. CONCLUSIONS: Peanut skin prick test wheal size is a robust predictor of clinical peanut reactivity. The findings of this study may be useful in guiding clinician decision-making regarding peanut allergy diagnostics.

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)0.003
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 score0.035
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.118
GPT teacher head0.407
Teacher spread0.289 · 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".

Quick stats

Citations5
Published2017
Admission routes4
Has abstractyes

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