Patch-Testing with Hairdressing Chemicals
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".