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Record W2268788193 · doi:10.2310/6620.2010.10061

Patch-Testing with Hairdressing Chemicals

2011· article· en· W2268788193 on OpenAlexvenueno aff
Michael Z. Wang, Sara A. Farmer, Donna M. Richardson, Mark D.P. Davis

Bibliographic record

VenueDermatitis · 2011
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePatch testDermatologyPatch testingSeries (stratigraphy)Allergic contact dermatitisContact dermatitisCase seriesSurgeryAllergyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Hairdressing chemicals may be associated with allergic contact dermatitis. OBJECTIVE: To review our experience of patch-testing with hairdressing chemicals. METHODS: We reviewed results from patients who underwent patch testing with our standard allergen series (including 15 hairdressing chemicals) and a supplementary "hairdresser series" (18 additional hairdressing chemicals) at Mayo Clinic (Rochester, MN; Scottsdale, AZ; and Jacksonville, FL) from January 1, 2000, through December 31, 2008. RESULTS: Two hundred ten patients (mean age, 53.8 years [SD, 16.9 yr]; female, 94.8%) were patch-tested. The most common sites of dermatitis were the scalp, face, and hands. Patients had widely varying occupations. The most common occupations were cosmetologist (10.5%), housewife (9.5%), and beautician (5.2%); 14.3% were retired. The hairdresser series detected 13 additional patients with allergies (6.4%; 204 patients tested with both series) who would not have been detected with the standard allergen series alone. The highest allergic patch-test rates in the supplemental hairdresser series were with ammonium persulfate (14.4%), 4-aminoazobenzene (13.4%), and pyrogallol (9.1%). CONCLUSIONS: Patch-testing with hairdressing-specific chemicals (standard series plus supplemental hairdresser series) was appropriate for numerous clinical situations and was not limited to patients in hair care occupations. The supplemental hairdresser series helped identify more patients than would have been identified with the standard series alone.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.654

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 head0.043
GPT teacher head0.244
Teacher spread0.200 · 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 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

Citations33
Published2011
Admission routes1
Has abstractyes

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