Etanercept Concentration in Patients with Rheumatoid Arthritis and Its Potential Influence on Treatment Decisions: A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: For patients with rheumatoid arthritis (RA), recommendations are inconclusive about whether tumor necrosis factor-α (TNF-α)-blocker therapy should be evaluated at 3 or 6 months. Biomarkers are needed to predict at 3 months which patients would benefit from further treatment because of nonoptimal response. Our objective was to investigate whether serum etanercept (ETN) concentrations and anti-ETN antibodies at 3 months are predictors of clinical response to ETN at 6 months in patients with RA in terms of European League Against Rheumatism criteria and Disease Activity Score in 28 joints (DAS28). METHODS: Between 2009 and 2010, we included 19 women with active RA who were candidates for ETN therapy. Response criteria were evaluated at 3 and 6 months. Serum concentrations of ETN and anti-ETN antibodies were measured by ELISA at baseline and at 3 and 6 months. RESULTS: Eighteen patients completed followup. Three-month ETN concentrations were lower for 6-month nonresponders than responders (p = 0.03). Three-month ETN levels correlated significantly with change in DAS28 between baseline and 6 months (r = -0.62, p = 0.006). The best predictor of response at 6 months was observed with an ETN threshold of 3.1 μg/ml at 3 months. No anti-ETN antibodies were found. CONCLUSION: ETN concentrations at 3 months predict response to ETN therapy at 6 months. Low ETN concentrations could explain the absence of response to ETN, suggesting that patients with low ETN levels could benefit from increased ETN dose or earlier interruption of treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".