Positive Patch-Test Reactions to Propylene Glycol: A Retrospective Cross-Sectional Analysis from the North American Contact Dermatitis Group, 1996 to 2006
Bibliographic record
Abstract
BACKGROUND: Propylene glycol (PG) may cause allergic or irritant contact dermatitis. It primarily functions as a vehicle, solvent, or emulsifier in cosmetics and topical medications. OBJECTIVES: To characterize the prevalence of positive patch-test reactions to PG and the epidemiology of affected patients. METHODS: Retrospective analysis of cross-sectional data compiled by the North American Contact Dermatitis Group (NACDG) from 1996 to 2006. RESULTS: Of 23,359 patients, 810 (3.5%) had allergic patch-test reactions to 30% PG; 12.8% of the reactions were of definite clinical relevance (positive reaction to a personal product containing PG), 88.3% were considered to be currently relevant (definite, probable, or possible relevance), and 4.2% of reactions were occupation related, most commonly to mechanical and motor vehicle occupations. Common sources of PG were personal care products (creams, lotions, and cosmetics, 53.8%), topical corticosteroids (18.3%), and other topical medicaments (10.1%). In patients positive only to PG (n = 135), the face was most commonly affected (25.9%), followed by a scattered or generalized pattern (23.7%). The most common concomitant reactions included reactions to Myroxilon pereirae, fragrance mix, formaldehyde, bacitracin, methyldibromoglutaronitrile/phenoxyethanol, carba mix, and tixocortol pivalate. CONCLUSIONS: In this select population of patients referred for patch testing, allergic reactions to PG were often currently clinically relevant but were rarely related to occupation. The most common sources were personal care products and topical corticosteroids.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".