Topical treatment of psoriasis: questionnaire results on topical therapy as long-term continuous treatment and use on specific body sites
Bibliographic record
Abstract
BACKGROUND: Currently, no formalized international consensus guidelines exist to direct optimal topical treatment including long-term treatment. OBJECTIVE: In this survey, we aim to examine if and which topicals are used in clinical practice in long-term continuous treatment of psoriasis and how topicals are used in treating specific sites of the body. METHODS: A questionnaire was distributed electronically to dermatologists from the International Psoriasis Council (IPC) representing 26 countries. RESULTS: The top three topicals used across all severities of disease were topical corticosteroids, vitamin D analogs, and potent topical corticosteroids in combination with vitamin D analogs. On locations where the skin is thin, flexural and genital psoriasis, lower potency topical corticosteroids were used, whereas on other sites, in particular in palmoplantar psoriasis, superpotent topical corticosteroids and combination vitamin D analogs/corticosteroids were used. CONCLUSIONS: It is relevant to optimize localized therapy for all severities of psoriasis reconciling disease activity (stable vs. unstable disease), localization of the lesions and the individual patient and his/her perspectives on disease control. Topical therapies are valuable treatments for classical mild disease and may have a position in some patients with more severe manifestations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".