Frequency of Allergenic Ingredients in Antifungal Creams
Bibliographic record
Abstract
BACKGROUND: Allergic contact dermatitis (ACD) may occur secondary to topical antifungals containing potential allergens in their vehicles. Variation of allergenic ingredients among commonly used antifungal creams (AFCs) has not been well characterized. OBJECTIVE: The study goal was to assess the frequency of allergenic ingredients in 4 commonly used topical AFCs. METHODS: Topical AFCs (clotrimazole, ketoconazole, miconazole, and terbinafine) were selected, and the ingredient lists for these products were obtained from the US Food and Drug Administration's Online Label Repository via a proprietary name search. A systematic literature review was performed using the ingredient name on MEDLINE (PubMed) database to identify reports of ACD confirmed by patch testing. RESULTS: Of the 20 ingredients analyzed, 6 had frequent allergenic potential. Propylene glycol was the most common cause of ACD identified in the literature and is an ingredient in ketoconazole 2% and miconazole nitrate 2%. Ketoconazole 2% and miconazole nitrate 2% creams contained the highest number of potential allergens (n = 3) among the 4 creams analyzed. CONCLUSIONS: Of the 4 creams, terbinafine hydrochloride 1% and clotrimazole 1% contained the least number of potential allergenic ingredients. Awareness of the allergenic potential of commonly used AFCs may help health care providers when evaluating patients with ACD.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.002 | 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".