Drug Survival of Biologics for Psoriasis in Canada: Real-World Patterns and Implications
Bibliographic record
Abstract
Biologic therapies have transformed psoriasis management, and real-world data are essential for understanding long-term treatment success. Drug survival—how long patients remain on biologics—varies across drug classes and is generally higher in biologic-naïve patients. Canadian studies demonstrate that biologics from the interleukin (IL)-12/23, IL-17 and IL-23 classes exhibit longer persistence rates. However, drug survival alone is not a comprehensive measure of efficacy, because it may not account for dose modifications or interval adjustments that frequently occur in clinical practice. As drug survival reflects the duration of time on a single therapy, it may not adequately capture the rate at which patients switch between biologic treatments. The switch rate captures how often patients change biologics within classes or to a different class, reflecting treatment modification due to inefficacy or side effects. Although Canadian data on biologic switch rates are limited, U.S. findings show that IL-23 inhibitors have the lowest rates. Collectively, the evaluation of drug survival, therapeutic modifications, and switch rates in real-world settings offers a comprehensive framework to inform dermatological clinical practice in Canada.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".