Comparative analysis of persistence and remission with guselkumab <i>versus</i> secukinumab and ixekizumab in the United States
Bibliographic record
Abstract
Purpose: Real-world data comparing long-term performance of interleukin (IL)-23 and IL-17 inhibitors in psoriasis are limited. This study compared treatment persistence and remission among patients initiating guselkumab versus IL-17 inhibitors.Methods: Adults with psoriasis initiating guselkumab, secukinumab, or ixekizumab treatment (index date) were identified from Merative™ MarketScan® Research Databases (01/01/2016–10/31/2021). Persistence was defined as no index biologic supply gaps of twice the labeled maintenance dosing interval. Remission was defined using an exploratory approach as index biologic discontinuation for ≥6 months without psoriasis-related inpatient admissions and treatments.Results: There were 3516 and 6066 patients in the guselkumab versus secukinumab comparison, and 3805 and 4674 patients in guselkumab versus ixekizumab comparison. At 18 months, the guselkumab cohort demonstrated about twice the persistence rate as secukinumab (hazard ratio [HR] = 2.15; p < 0.001) and ixekizumab cohorts (HR = 1.77; p < 0.001). At 6 months after index biologic discontinuation, the guselkumab cohort was 31% and 40% more likely to achieve remission than secukinumab (rate ratio [RR] = 1.31; p < 0.001) and ixekizumab cohorts (RR = 1.40; p < 0.001).Conclusions: Guselkumab was associated with greater persistence and likelihood of remission than IL-17 inhibitors, indicating greater disease control and modification potential.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".