Real-world Drug Survival of Biosimilar SB5 vs GP2017 Following a Mandatory Non-medical Switch from Adalimumab Originator for Psoriasis: A Nationwide Cohort Study
Bibliographic record
Abstract
Biosimilars are designed to be analogous to the biologic originators. However, comprehensive comparisons between biosimilars are limited and lacking for patients with psoriasis. This study's objective was to compare 1-year drug survival of the 2 adalimumab biosimilars, GP2017 with SB5, following a non-medical mandatory switch from the adalimumab originator. Based on the national clinical database DERMBIO, this nationwide cohort study included all patients with psoriasis treated with the adalimumab originator who underwent a non-medical mandatory switch to GP2017 or SB5. The study included 525 patients switching from adalimumab originator to 1 of 2 biosimilars: GP2017 (n=267 patients) or SB5 (n=258 patients). When comparing the drug survival of the biosimilars, the hazard ratio was 1.11 (95% confidence interval, 0.58-2.12) for SB5 with GP2017 as reference. In the sensitivity analysis investigating changes in PASI, no differences were found when comparing the GP2017 group and the SB5 group at 120 days of follow-up (1.9% vs 1.6% improved, 1.9% vs 2.0% worsened, and 43.1% vs 40.9% remained unchanged). When comparing GP2017 and SB5, no discernible differences were found in drug survival or effectiveness based on PASI. Determining drug survival and effectiveness could benefit patients and clinicians in treatment decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".