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Record W162582067 · doi:10.2310/6620.2008.07018

Preservatives and Skin Sensitization Quantitative Risk Assessment

2008· article· en· W162582067 on OpenAlexvenueno aff
David A. Basketter, Catherine J. Clapp, B. Safford, Ian R. Jowsey, Pauline McNamee, Cindy A. Ryan, Frank G. Gerberick

Bibliographic record

VenueDermatitis · 2008
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCosmeticsPreservativeAllergic contact dermatitisSkin sensitizationSensitizationRisk assessmentDermatologyToxicologyRisk analysis (engineering)Food scienceAllergyImmunologyComputer sciencePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Preservatives are an unfortunately common cause of allergic contact dermatitis (ACD). Often, this is in association with exposure to cosmetics or medicaments. Recently, a quantitative risk assessment (QRA) approach to the quantitation of safe exposure levels for sensitizers has been promulgated as a more effective tool for the identification of acceptable levels of potential sensitizers in consumer products. OBJECTIVE: To assess this QRA approach, which facilitates the prediction of acceptable exposure levels to skin sensitizers in consumer products, levels that are normally below the threshold for the induction of skin sensitization. METHODS: Retrospective QRA analysis on four preservatives in five consumer product types. RESULTS: The analysis shows that functional levels of preservatives may be somewhat above an ideal exposure level for some product types, an outcome that is consistent with the clinical picture. CONCLUSION: QRA represents a new tool that in the future should be used in combination with the assessment of microbiologic protection needs of specific product types to limit the problem of preservative ACD.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.289
Teacher spread0.267 · 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

Citations57
Published2008
Admission routes1
Has abstractyes

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