Treatment of Psoriasis with Secukinumab in Challenging Patient Scenarios: A Review of the Available Evidence
Bibliographic record
Abstract
Psoriasis (PsO) is a common, systemic, chronic, inflammatory disease characterized by key clinical symptoms, including itching, pain, and scaling. PsO is associated with a high prevalence of comorbidities, including other autoimmune diseases and malignancies. Furthermore, special populations, such as pregnant, pediatric, and elderly patients, and those with erythrodermic PsO, are challenging to treat and require tightly monitored disease and treatment management. Because certain populations have demographic or clinical characteristics that can affect the presentation of PsO and complicate treatment responses, these patient populations are largely excluded from clinical trials; therefore, most clinical evidence for the treatment of these patients is derived from case reports and series. Secukinumab, a fully human monoclonal interleukin-17A antibody, has been shown in several clinical trials to be effective and safe for the treatment of PsO; however, these studies offer only limited data on the use of secukinumab in patients with chronic illnesses or in special populations. This review explores the use of secukinumab for PsO in special populations, including pregnant women, children, elderly people, patients with erythrodermic PsO, and those with chronic illnesses, including latent tuberculosis, hepatitis B and C, HIV, multiple sclerosis, and malignancies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".