Duration of etanercept treatment and reasons for discontinuation in a cohort of juvenile idiopathic arthritis patients
Bibliographic record
Abstract
OBJECTIVE: Since 2004, juvenile idiopathic arthritis (JIA) patients treated with etanercept and/or MTX have been monitored in the British Society for Paediatric and Adolescent Rheumatology Biologics and New Drug Register. Here, we report the duration of etanercept use for the first 5 years of the register and reasons for discontinuation. METHODS: Disease subtype and activity, comorbidity, treatment efficacy and safety data were recorded. Etanercept discontinuation was defined as stopping the drug because of disease remission or treatment failure. Time to discontinuation was explored using Kaplan-Meier survival analysis with remaining patients censored at 5-year follow-up. RESULTS: A total of 483 etanercept-treated JIA patients were enrolled from 30 UK centres, representing 941 patient-years of follow-up. A total of 100 (20.7%) patients discontinued etanercept; 9 due to disease control, 88 because of treatment failure, 2 for unknown reasons and 1 because of a change in diagnosis. Of the 53 patients in whom etanercept was perceived to be ineffective at controlling the inflammation, 48 were prescribed other biologic drugs [26/48 (54%) infliximab]. In 21 patients with intolerance, infections, CNS events and a few isolated events were associated with discontinuation. Using Kaplan-Meier analysis, at 5 years 69% (95% CI 61, 77%) had not experienced treatment failure. Discontinuation of etanercept for inefficacy was associated with systemic arthritis subtype [odds ratio (OR) 2.55, 95% CI 1.27, 5.14], chronic anterior uveitis (OR 2.39, 95% CI 1.06, 5.35) and inefficacy of MTX before starting etanercept (OR 8.3, 95% CI 1.14, 60.58). CONCLUSIONS: In a cohort of JIA patients treated with etanercept and followed for a median of 2 years (maximum 5 years), the majority (69%) remain on the drug.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".