Characterization of insufficient responders to ustekinumab in patients with moderate-to-severe psoriasis in the US Corrona Psoriasis Registry
Bibliographic record
Abstract
Objective Biologic therapies have revolutionized the management of moderate-to-severe psoriasis; however, there are a limited number of US real-world studies characterizing patients based on response to these treatments. This study examined characteristics at enrollment and change in outcomes of US patients with moderate-to-severe psoriasis who achieved insufficient responses with ustekinumab.Methods This study included patients enrolled in the Corrona Psoriasis Registry from April 2015 to June 2018 who initiated ustekinumab at enrollment and who were stratified based on achievement of psoriasis body surface area improving to <3% or by 75% from enrollment to the 6-month follow-up visit (response vs insufficient response). Patient demographics and disease characteristics were described at enrollment, and changes in outcomes were assessed at 6-month follow-up for ustekinumab responders and insufficient responders.Results Of the 178 patients who initiated ustekinumab in the Corrona Psoriasis Registry and had ≥1 follow-up visit, 99 (55.6%) were classified as responders at the 6-month follow-up visit. Logistic regression modeling showed that increasing age was significantly associated with a decreased likelihood of achieving a response (OR, 0.981 [95%CI, 0.962–0.999]; p = .049).Conclusions These findings may help dermatologists characterize patients with moderate-to-severe psoriasis who have inadequate responses to biologic treatments.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".