Malignancy rates through 5 years of follow-up in patients with moderate-to-severe psoriasis treated with guselkumab: Pooled results from the VOYAGE 1 and VOYAGE 2 trials
Bibliographic record
Abstract
BACKGROUND: Malignancy risk surveillance among patients receiving long-term immunomodulatory psoriasis treatments remains an important safety objective. OBJECTIVE: To report malignancy rates in patients with moderate-to-severe psoriasis treated with guselkumab for up to 5 years versus general and psoriasis patient populations. METHODS: Cumulative rates of malignancies/100 patient-years (PY) were evaluated in 1721 guselkumab-treated patients from VOYAGE 1 and 2. Malignancy rates (excluding nonmelanoma skin cancer [NMSC]) were compared with rates in the Psoriasis Longitudinal Assessment and Registry. Standardized incidence ratios comparing malignancy rates (excluding NMSC and cervical cancer in situ) between guselkumab-treated patients and the general US population using Surveillance, Epidemiology, and End Results data were calculated, adjusting for age, sex, and race. RESULTS: Of 1721 guselkumab-treated patients (>7100 PY), 24 had NMSC (0.34/100PY; basal:squamous cell carcinoma ratio, 2.2:1), and 32 had malignancies excluding NMSC (0.45/100PY). For comparison, the malignancy rate excluding NMSC was 0.68/100PY in the Psoriasis Longitudinal Assessment and Registry. Malignancy rates (excluding NMSC/cervical cancer in situ) in guselkumab-treated patients were consistent with those expected in the general US population (standardized incidence ratio = 0.93). LIMITATIONS: Inherent imprecision in determining malignancy rates. CONCLUSIONS: In patients treated with guselkumab for up to 5 years, malignancy rates were low and generally consistent with rates in general and psoriasis patient populations.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| 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 | 0.001 | 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".