Bibliographic record
Abstract
BACKGROUND: Whereas allergy to vehicle ingredients (ie, excipients and preservatives) in topical steroid vehicles is well recognized, there are no data regarding which vehicle ingredients are in common use or on which vehicles and active molecules are associated with which ingredients. OBJECTIVE: To produce descriptive data on the use of allergenic vehicle ingredients in prescription topical corticosteroids. METHODS: The package insert for every steroid in widespread use in the United States was obtained from the manufacturer and used to generate an ingredient list for the product. RESULTS: There are seven vehicle ingredients that are commonly used in topical corticosteroid vehicles that are well-known allergens: propylene glycol, sorbitan sesquioleate, formaldehyde-releasing preservatives, parabens, methylchloroisothiazolinone/methylisothiazolinone, lanolin, and fragrance. Of 166 topical corticosteroids, 128 (including all creams) had at least one of these vehicle ingredients. More generic products were free of allergens than were branded products. Solutions and ointments were the least allergenic vehicles. The most commonly present potential allergens were propylene glycol and sorbitan sesquioleate. CONCLUSIONS: Most prescription topical corticosteroids have the potential to cause allergic contact dermatitis owing to vehicle ingredients. Dermatologists should be aware of this possibility and should consider prescribing agents that do not contain potentially allergenic vehicle ingredients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".