Most Common Co/Cross-Reactants Identified in <i>p</i> -Phenylenediamine–Allergic Patients and Impact on Available Alternative Hair Dyes
Bibliographic record
Abstract
Background p -Phenylenediamine (PPD) is a common component of hair dyes and is also a common contact sensitizer. Rates of sensitization to PPD, among patients presenting for patch testing, range between 2% and 12%. Given the rates of sensitization, hair dyes containing alternatives to PPD have been developed. Objective This study aimed to determine the most common co/cross-reactants in PPD-allergic patients and to assess if those co/cross-reactants are found in PPD-free hair dyes and thus limit their use by PPD-allergic patients. Methods We retrospectively reviewed all patch test results of patients presenting to the University of British Columbia Contact Dermatitis Clinic between January 2008 and June 2013. All patients were patch tested with a screening series of 65 to 80 allergens as well as supplemental allergens as clinically indicated. The American Contact Dermatitis Society Contact Allergen Management Program database was queried for hair dyes without PPD and each of the most common co/cross-reactants. Conclusions Co/cross-reacting to nickel, cobalt, ammonium persulfate, glyceryl thioglycolate, p -toluenediamine sulfate/base, black rubber mix, thiuram mix, or carba mix did not further restrict the number of dyes. Cross-reacting to 4-aminophenol restricted available dyes to 16. Positivity to any of the fragrances restricted patients to 1 nonpermanent dye.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".