A Novel Approach to Assess the Potency of Topical Corticosteroids
Bibliographic record
Abstract
The potencies of topical corticosteroid products have mainly been classified using clinical data but in some instances, the US Food and Drug Administration’s (FDA’s) vasoconstrictor assay (VCA) to assess the skin blanching response has also been used. However, the reported skin blanching response data were often based on a single visual reading and lack information on the dose (amount/quantity) or dose duration. Although several lists classifying potencies of various topical corticosteroid products have been published, the inherent potencies of topical corticosteroid raw materials used as active pharmaceutical ingredients (APIs) have not been investigated. The objective was to rank the inherent potencies of topical corticosteroid APIs and to standardize dosing such that the relevant compounds could be compared on a normalized molar basis. The potencies of clobetasol propionate, halcinonide, mometasone furoate, and fluocinolone acetonide were compared using the resulting Emax data following the fitting of the relevant response data to the Emax model where mometasone furoate > fluocinolone acetonide = clobetasol propionate > halcinonide. This ranking lists the respective inherent potencies of the APIs, which will facilitate the choice of a suitable candidate for incorporation into an appropriate topical corticosteroid product for a specific clinical indication.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".