Predictors of Flare Following Etanercept Withdrawal in Patients with Rheumatoid Factor–negative Juvenile Idiopathic Arthritis Who Reached Remission while Taking Medication
Bibliographic record
Abstract
OBJECTIVE: To evaluate the rate of flare after etanercept (ETN) withdrawal in patients with juvenile idiopathic arthritis (JIA) who attained clinical remission while taking medication, and to identify predictors of flare. METHODS: Patients were included with oligo- (oJIA) and rheumatoid factor-negative polyarticular JIA (pJIA) who received a first course of ETN for at least 18 months, maintained clinically inactive disease (CID) for at least 6 months during treatment, and were followed for 12 months after ETN withdrawal. Demographic and clinical features were collected at onset, at baseline (initiation of ETN), and at time of disease flare. RESULTS: After ETN withdrawal, 66 of the 110 patients enrolled (60%) flared with arthritis (of whom 7 flared with concurrent anterior uveitis; none with uveitis alone). The median time to flare was 4.3 months (interquartile range 2.5-6.4) with no evident differences between oJIA and pJIA. The number and type of joints involved at baseline and characteristics of ETN treatment/discontinuation were not associated with flare. Patients who flared were more frequently males (p = 0.034), positive for antinuclear antibody (ANA; p = 0.047), and had higher values of C-reactive protein (CRP; p = 0.012) at baseline. These variables remained significantly associated with flare in a multivariate logistic analysis, a model accounting for only 14% of the variability of the occurrence of the flare. CONCLUSION: Our results show that a significant proportion of patients with JIA who maintain CID for at least 6 months experience a relapse after ETN withdrawal. Male sex, presence of ANA, and elevated CRP at baseline were associated with higher risk of flare.
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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 | 0.005 |
| 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) | 0.001 | 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".