Contact Dermatitis Associated With Hair Care Products: A Retrospective Analysis of the North American Contact Dermatitis Group Data, 2001–2016
Bibliographic record
Abstract
Background Hair care products (HCPs) may cause both allergic contact dermatitis (ACD) and irritant contact dermatitis (ICD). Objectives The aims of the study were to determine the prevalence of HCP-associated ICD/ACD and to characterize relevant allergens. Methods This study is a retrospective analysis of North American Contact Dermatitis Group (NACDG) patch test data, 2001–2016. Results Of 38,775 patients tested, 3481 (9.0%) had positive patch test reactions associated with HCPs. The HCP-positive patients were significantly more likely to be female (79.9% vs 66.0%) and/or have primary sites of dermatitis on the face (32.0% vs 27.8%) or scalp (15.4% vs 2.2%) compared with the HCP-negative patients ( P < 0.0001). Of 4908 HCP-associated positive patch test reactions, 86.9% (n = 4263) were due to allergens on the NACDG screening series; p -phenylenediamine (35.8%), methylisothiazolinone (9.7%), methylchloroisothiazolinone/methylisothiazolinone (8.7%), and cocamidopropyl betaine (5.9%) were the most frequent. Most reactions (87.7%, 3736/4263) were currently clinically relevant. The most common job associated with 366 occupationally related NACDG HCP-associated allergens was hairdresser/cosmetologist (71.9%). Two hundred eighty-two patients (0.7%) had ICD associated with HCPs. Shampoo/conditioners were the most frequent source of NACDG HCP-associated reactions (47.3%) and HCP-associated ICD (45.0%). Conclusions Of the HCP-positive patients, 18.5% had HCP reactions to allergens not on the NACDG screening series, underscoring the importance of patch testing to expanded series in patients suspected of HCP 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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".