The National Psoriasis Foundation psoriasis treatment targets in real‐world patients: prevalence and association with patient‐reported outcomes in the Corrona Psoriasis Registry
Bibliographic record
Abstract
INTRODUCTION: The National Psoriasis Foundation (NPF) published treat-to-target guidelines for psoriasis, yet their applicability in clinical practice remains unknown. OBJECTIVES: To estimate the proportion of psoriasis patients meeting the NPF's body surface area (BSA) 'target' (≤1%) and 'acceptable' (≤3%) response criteria and the cross-sectional associations of these criteria with patient-reported outcomes (PROs) in the Corrona Psoriasis Registry. METHODS: Separately for three independent cross-sectional cohorts of patients at the (i) enrolment, (ii) 6-month and (iii) 12-month visits, we calculated the proportion of patients with BSA ≤1% and ≤3%. Furthermore, we calculated odds ratios estimating the risk of PROs associated with not meeting criteria in the 6-month cohort. RESULTS: The enrolment, 6- and 12-month cohorts included 2794, 1310 and 629 patients, respectively. At enrolment, 24% of patients had a BSA ≤ 1% and 41% a BSA ≤ 3%. In the 6-month cohort, 43%/64% had a BSA ≤ 1%/BSA ≤ 3%. In the 12-month cohort, 46%/69% of patients had a BSA ≤ 1%/BSA ≤ 3%. Patients not at target/acceptable criteria had higher odds for worse quality of life compared with those who were. CONCLUSION: While most patients at 6- and 12-month visits were at the 'acceptable' response, less than half were at the 'target' response despite systemic therapy. There remain unmet needs to optimize psoriasis therapy and further validate current treat-to-target guidelines.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.001 |
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".