Biologic Switching Among Nonsystemic Juvenile Idiopathic Arthritis Patients: A Cohort Study in the Childhood Arthritis and Rheumatology Research Alliance Registry
Bibliographic record
Abstract
OBJECTIVE: Biologic medications have significantly improved disease control and outcomes of patients with juvenile idiopathic arthritis (JIA). Current treatment recommendations suggest escalating therapy, including changing biologics if needed, when inactive or low disease activity is not attained. The patterns and reasons for switching biologics in clinical practice in North America are not well described. METHODS: We used the Childhood Arthritis and Rheumatology Research Alliance Registry and included individuals with JIA if they newly started a biologic after January 1, 2008, and had at least 12 months of subsequent observable time. Subjects with systemic JIA were excluded. We compared characteristics of switchers and nonswitchers using chi-square for categorical variables and Wilcoxon rank-sum test for continuous variables, and used linear regression for time analysis. RESULTS: Of the eligible children, 1361 with JIA in the registry started a biologic (94% tumor necrosis factor inhibitors [TNFi]). Median followup time was 30 months and 349 (26%) switched biologics. Among biologic switchers, ineffectiveness/disease flare was the most common reason for switch (202, 58%). The most common documented switch was from etanercept to another TNFi (221, 63%). The median time to switch to a second biologic decreased substantially from 55.2 months in 2008 to 7.2 months in 2016. CONCLUSION: In a multicenter cohort of patients with JIA starting a biologic, one-quarter switched to a second biologic, and the time to switching decreased in recent years. Additional studies should evaluate the outcomes and optimal timing of switching and preferred sequence of biologic use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".