Etanercept (Enbrel) in the treatment of juvenile idiopathic arthritis
Bibliographic record
Abstract
INTRODUCTION: Juvenile idiopathic arthritis (JIA) is a relatively common multidimensional and heterogeneous chronic disease of childhood. Children with JIA are at risk for significant morbidity in terms of joint damage, impairments in physical function and health-related quality of life. Outcomes for children with JIA have significantly improved with the use of biologic therapies in the past 15 years, with the most clinical experience being with etanercept . AREAS COVERED: Basic pharmacokinetic and pharmacodynamic data for etanercept will be highlighted. This article will review the clinical trials and open-label registry data for the efficacy and safety of etanercept for use in JIA. EXPERT OPINION: Etanercept is very effective for the treatment of JIA. Data from clinical trials and open-label studies support its clinical efficacy in 80% of patients which appears to be sustained over several years for the majority of treated patients. The safety profile is also acceptable with a serious adverse event rate of 0.03 - 0.12 per patient-year. Further research is needed to evaluate any possible link between biologic therapy, JIA and malignancy, to obtain more long-term safety data, and to document improvements in quality of care and cost-benefit for associated with biologic therapies which may additionally assist in access to these medications. Further, identification of potential clinical or laboratory markers allowing for prediction of response and timing of starting and cessation of this biologic therapy are urgently required.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".