A Description of Treatment Patterns of Psoriasis by Medical Providers and Disease Severity in US Women
Bibliographic record
Abstract
BACKGROUND: Studies on treatment patterns of psoriasis are valuable to evaluate how efficiently individuals with psoriasis are treated and may facilitate improved outcomes for these patients. OBJECTIVE: To describe treatment patterns of psoriasis among US women. METHODS: In the Nurses' Health Study II (NHS II), a prospective study of female nurses, 2107 women reported to have a diagnosis of psoriasis made by a clinician. We sent them the Psoriasis Screening Tool-2, a validated diagnostic tool for psoriasis, which queries age at diagnosis, treatments, type of psoriasis lesions, body surface area involved, and the provider who made the diagnosis. RESULTS: A total of 1382 women completed and returned the survey, with 1243 of them validated for having psoriasis. 30% of the patients were diagnosed by non-dermatologists. 79% of the patients reported mild, 17% moderate and 4% severe disease. Psoriasis phenotypes were as follows: plaque 41%, scalp 49%, inverse 27%, nail 22% and palmoplantar 15%. Treatment patterns for mild psoriasis were as follows: only topical treatment 58%, systemic therapy and/or phototherapy 16% and no treatment 26%. Treatment patterns for moderate-to-severe disease were as follows: only topical treatment 42%, systemic therapy and/or phototherapy 47% and no treatment 11%. CONCLUSION: The majority of women in NHS II with psoriasis have mild disease. A large proportion of psoriasis patients were diagnosed by non-dermatologists. More than half of people with moderate-to-severe disease received no treatment or only topical medications. A considerable percentage of people with psoriasis reported phenotypes other than chronic plaque psoriasis.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".