Biologic and small molecule therapies for psoriasis in individuals with Down syndrome: Two cases and a systematic review
Bibliographic record
Abstract
Down syndrome (DS), also known as trisomy 21, is a genetic condition linked to a higher prevalence of skin disorders, including psoriasis, which affects up to 8% of individuals. DS patients with psoriasis present unique management considerations, including a theoretical increased risk of infectious complications with immunosuppressive therapies. This report includes two cases and a systematic review summarizing available evidence on psoriasis characteristics and treatment outcomes in individuals with DS. We report two DS patients with psoriasis demonstrating variable therapeutic responses: one controlled with acitretin and another requiring secukinumab after multiple treatment failures. To contextualize these findings, we conducted a systematic review following PRISMA guidelines, identifying 10 studies comprising 37 DS patients with psoriasis. Methotrexate was the most frequently failed therapy. Biologics targeting IL-17 and IL-23 pathways achieved the highest rates of complete resolution. These findings reflect Th1/Th17-driven inflammation in DS and highlight the need for individualized, pathway-specific management strategies.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".