Positive Patch-Test Reactions to Propylene Glycol: A Retrospective Cross-Sectional Analysis from the North American Contact Dermatitis Group, 1996 to 2006
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it