Prurigo Nodularis onset during secukinumab treatment of psoriasis: a case report
Bibliographic record
Abstract
BACKGROUND: Secukinumab has been approved by the U.S. FDA and the European Medicines Agency for the treatment of moderate-to-severe plaque psoriasis and psoriatic arthritis with the documented adverse effects. Here we reported in one case that a new symptom, Prurigo Nodularis (PN), developed during the programmed dosing of secukinumab. CASE INTRODUCTION: A 22-years-old male with a 6-month history of severe plaque psoriasis vulgaris was presented to the dermatology clinic two weeks after the fifth serial weekly doses of secukinumab, for the reason of the outbreaks of multiple erythematous papules and pruritus nodules on the trunk and extremities. Physical examination showed that psoriatic rash were under effective control with the previous targeted therapy of secukinumab for plaque psoriasis vulgaris, but new dermatologic condition was spotted with multiple edematous red firm papules on the trunk and extremities, in the form of soy or hemispherical nodules, red in color, firm to touch, with some ulcerated crusts visible at tops, but negative Auspitz sign. Pathological examination confirmed these papules as PN. CONCLUSION: This case report is shared to inform clinicians about an unannounced adverse effect of the secukinumab in the treatment of psoriasis, and it is recommended that patients be carefully informed of the possible risk of PN before starting treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".