Clinical Efficacy of Etanercept Versus Sulfasalazine in Ankylosing Spondylitis Subjects with Peripheral Joint Involvement
Bibliographic record
Abstract
OBJECTIVE: Etanercept, a fully human tumor necrosis factor soluble receptor, is effective in treatment of ankylosing spondylitis (AS). Current guidelines suggest sulfasalazine (SSZ) treatment as initial therapy for the management of patients with AS with peripheral arthritis versus therapy with biologics. We compared the efficacy of etanercept with SSZ in patients with AS with peripheral joint involvement. METHODS: The efficacy of etanercept 50 mg once weekly was compared with that of SSZ up to 3 g daily in subjects with ≥ 1 swollen peripheral joint at baseline, using data from a 16-week randomized double-blind study in subjects with AS. Efficacy was assessed by the Assessment in AS criteria and the Bath AS Disease Activity, Functional, and Metrology indices. The last observation carried forward method was used for imputation of missing values. RESULTS: Of 566 subjects included in original study, 181 (etanercept 121; SSZ 60) had ≥ 1 swollen peripheral joint and 364 (etanercept 250; SSZ 124) had none at baseline. AS patients treated with etanercept showed significantly greater improvement than those treated with SSZ in all joint assessments regardless of swollen joint involvement. CONCLUSION: In this analysis, etanercept was significantly more effective than SSZ for management of patients with AS and peripheral joint involvement.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".