Efficacy of guselkumab versus secukinumab in subpopulations of patients with moderate-to-severe plaque psoriasis: results from the ECLIPSE study
Bibliographic record
Abstract
PURPOSE: Guselkumab, an interleukin (IL)-23 inhibitor, effectively treats moderate-to-severe plaque psoriasis. MATERIALS AND METHODS: = 514) through week 44. Efficacy (at least a 90% and 100% improvement from baseline in Psoriasis Area and Severity Index [PASI 90 and PASI 100], Investigator's Global Assessment [IGA] 0/1, and IGA 0) was analyzed across subpopulations defined by baseline: age (<45, 45 to <65, and ≥65 years old), body weight, body mass index (BMI), psoriasis disease severity (body surface area, disease duration, PASI, and IGA), psoriasis by body regions (head, trunk, upper and lower extremities), and prior psoriasis medication history at week 48. RESULTS: Overall, 1048 patients were randomized. At week 48, numerically greater proportions of patients achieved PASI 90, PASI 100, IGA 0/1, and IGA 0 with guselkumab vs. secukinumab regardless of baseline age, body weight, BMI, disease severity, body region, and prior medication. The largest differences were in patients ≥65 years old and patients weighing >100 kg. CONCLUSIONS: Guselkumab treatment provided greater efficacy vs. secukinumab at week 48 in most subpopulations of patients with psoriasis.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".