Navigating the Misdiagnosis of Pityriasis Rubra Pilaris and Successful Treatment With Guselkumab: A Case Report of Dual Biologic Therapy
Bibliographic record
Abstract
Pityriasis rubra pilaris (PRP) is a rare and chronic dermatologic condition often misdiagnosed due to its clinical resemblance to psoriasis. It significantly impacts quality of life due to its clinical presentation and systemic discomfort. Conventional treatments, including retinoids, corticosteroids, and methotrexate, often yield suboptimal outcomes, particularly in refractory cases. Recently, biologic therapies have shown promise for PRP management. This case report describes the successful treatment of PRP with guselkumab. A 57-year-old male presented with an eight-year history of refractory papulosquamous disorder affecting 70% of his body surface area, initially diagnosed and treated as psoriasis. The patient had failed topical steroids, topical calcineurin inhibitors, sulfasalazine, and methotrexate. He had a past medical history of asthma treated with dupilumab. On skin exam, there were orange-red scaly plaques on the scalp, trunk, upper and lower extremities, buttocks, and genitals. Some of the plaques coalesced in areas to confluence, making PRP a presumptive diagnosis. Diagnosis was confirmed by three punch biopsies, and treatment with guselkumab 100mg subcutaneous injection was initiated. The patient noted improvement after the first loading dose and achieved complete skin clearance following the second dose four weeks later. At his one-year follow-up, he maintained full clearance while receiving guselkumab 100 mg subcutaneously every eight weeks. This case highlights the potential of guselkumab as an effective treatment for refractory PRP, even in cases of extensive disease. It underscores the importance of accurate diagnosis through biopsy and demonstrates the feasibility of concurrent biologic therapies for managing comorbid inflammatory conditions. Further research is needed to optimize biologic treatment strategies and enhance understanding of dual biologic use in complex dermatologic cases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".