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Record W3093135428 · doi:10.1097/der.0000000000000674

Expanding Patch Testing Beyond the Baseline Series

2020· article· en· W3093135428 on OpenAlexaffvenueabout
Catherine Besner Morin, Denis Sasseville

Bibliographic record

VenueDermatitis · 2020
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsMcGill University Health CentreMontreal General Hospital
Fundersnot available
KeywordsMedicineSeries (stratigraphy)CosmeticsBaseline (sea)Patch testingContact dermatitisGeologyImmunologyPathologyPaleontology

Abstract

fetched live from OpenAlex

BACKGROUND: Testing cosmetics and their ingredients is essential to avoid missing relevant allergens and to monitor fluctuating incidence of hypersensitivity. OBJECTIVE: The aim of this study was to review the usefulness of patch testing with a customized antimicrobials, vehicles, and cosmetics (AVC) series over 15 years at a single Canadian site. METHODS: Between January 1, 2005, and December 31, 2019, patients suspected of having cosmetics allergy were patch tested with a 40-allergen AVC series in addition to the North American Contact Dermatitis Group standard screening series. We reviewed the patch test results of 2868 patients. RESULTS: We consecutively patch tested with the baseline series 6103 patients, of which 2868 (47%) were also tested with the AVC series. Of 53 different allergens that were tested at some point, 26 remained in the series throughout the 15-year span. The most common positive allergens were thimerosal (4.52%), polyvidone-iodine (2.25%), propolis (2.06%), sodium metabisulfite (1.94%), dodecyl gallate (1.53%), carmine (1.10%), lauryl glucoside (1.01%), sandalwood oil (0.7%), and tert-butylhydroquinone (0.7%). CONCLUSIONS: Although the expansion of the North American Contact Dermatitis Group standard screening series has decreased the yield from the AVC series from 21.1% to 13.9%, it still remains a useful adjunct for patients suspected of having cosmetics or disinfectants allergy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.255
Teacher spread0.226 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations9
Published2020
Admission routes3
Has abstractyes

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