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: 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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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