Response to Ustekinumab in a Patient with Severe Psoriasis when Adalimumab Dose Escalation Fails
Bibliographic record
Abstract
Objectives: To report a case of response to ustekinumab in a patient with severe psoriasis when adalimumab dose escalation fail.Case Description: A 35-year-old Caucasian male was diagnosed as having psoriasis. He was started on methotrexate followed cyclosporine. Despite these treatments, the patient experienced an abrupt deterioration of his psoriasis [PASI 35]. The patient started treatment with adalimumab: initial dose of 80mg followed by 40mg in week 2. At week 3 he continued on adalimumab 40mg/two weeks with significant reduction of psoriasis. After 8 months of adalimumab therapy, he referred that adalimumab was effective only during the first week of treatment. The patient gave his written informed consent for adalimumab dose intensification. He started adalimumab 40mg/week, with an improvement of psoriasis. After 7 months of adalimumab dose intensification, the psoriasis worsened [PASI score =25 and CDLQI score 20]. We discontinued adalimumab and started therapy with ustekinumab, 45mg subcutaneously, was administered at weeks 0, 4 and every 12 weeks. The clinical response was impressive; at week 12 a PASI 90 response was achieved while the CDLQI score fell to the scale of 7. Efficacy was maintained after a 12 months of ustekinumab therapy.Conclusions: This case report provides valuable insight into the efficacy and tolerability of ustekinumab in a patient with severe psoriasis when adalimumab dose escalation fails. To our knowledge this is the first case published to date that describes the clinical efficacy of ustekinumab when adalimumab intensification dose escalation fails.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| 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".