Characterization of insufficient responders to anti-tumor necrosis factor therapies in patients with moderate to severe psoriasis: real-world data from the US Corrona Psoriasis Registry
Bibliographic record
Abstract
Objective Biologic therapies have dramatically changed the management of moderate to severe psoriasis; however, few US real-world studies characterize the unmet needs of patients who do not respond to biologic therapies. This study examined the characteristics at enrollment of patients with moderate to severe psoriasis who had insufficient responses to anti-tumor necrosis factor therapies (anti-TNFs).Methods Patients enrolled in the Corrona Psoriasis Registry from April 2015 to June 2018 who initiated an anti-TNF at enrollment were stratified on the basis of body surface area (BSA) improvement to <3% or a 75% improvement from enrollment to the 6-month follow-up visit (response versus insufficient response). Patient demographics and disease characteristics were described at enrollment, and changes in outcomes were assessed at 6-month follow-up for those who received anti-TNFs.Results Of 180 anti-TNF initiators who had ≥1 follow-up visit, 50.6% were classified as responders. Logistic regression modeling showed that female sex was significantly associated with a decreased likelihood of achieving a response (OR = 0.534, 95% CI = 0.289–0.988, p = .046).Conclusion Despite the small sample size and short follow-up period, these findings may help dermatologists to identify patients with moderate to severe psoriasis who have unmet treatment needs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".