Evidence map›Paper›PMID 31796875›Full record

ArticleScientific reports2019

Frequentist and Bayesian approaches for food allergen risk assessment: risk outcome and uncertainty comparisons.

Sophie Birot, Amélie Crépet, Benjamin C Remington, Charlotte B Madsen, Astrid G Kruizinga, Joseph L Baumert, Per B Brockhoff

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sophie BirotDTU Compute, Richard Petersens Plads, DK-2800 Kgs, Lyngby, Denmark.
Amélie CrépetANSES, French Agency for Food, Environmental and Occupational Health Safety, 14 rue Pierre et Marie Curie, 94701, Maisons-Alfort, France.
Benjamin C RemingtonThe Netherlands Organization for Applied Scientific Research (TNO), Zeist, The Netherlands.
Charlotte B MadsenNational Food Institute, Technical University of Denmark, Lyngby, Denmark. charm@food.dtu.dk.ORCID http://orcid.org/0000-0002-9086-7120
Astrid G KruizingaThe Netherlands Organization for Applied Scientific Research (TNO), Zeist, The Netherlands.
Joseph L BaumertFood Allergy Research and Resource Program, Department of Food Science & Technology, University of Nebraska, 143 Food Industry Complex, Lincoln, Nebraska, 68583-0919, United States.
Per B BrockhoffDTU Compute, Richard Petersens Plads, DK-2800 Kgs, Lyngby, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Peer-reviewed probabilistic methods already predict the probability of an allergic reaction resulting from an accidental exposure to food allergens, however, the methods calculate it in different ways. The available methods utilize the same three major input parameters in the risk model: the risk is estimated from the amount of food consumed, the concentration of allergen in the contaminated product and the distribution of thresholds among allergic persons. However, consensus is lacking about the optimal method to estimate the risk of allergic reaction and the associated uncertainty. This study aims to compare estimation of the risk of allergic reaction and associated uncertainty using different methods and suggest improvements. Four cases were developed based on the previous publications and the risk estimations were compared. The risk estimation was found to agree within 0.5% with the different simulation cases. Finally, an uncertainty analysis method is also presented in order to evaluate the uncertainty propagation from the input parameters to the risk.

Indexed as

Models, StatisticalUncertaintyAllergensBayes TheoremComputer SimulationFood HypersensitivityHumansMonte Carlo MethodRisk AssessmentRisk FactorsAllergens

Identifiers

PMID31796875
PMCPMC6890679

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.