Community-based evaluation of etanercept in patients with rheumatoid arthritis.
Bibliographic record
Abstract
OBJECTIVES: Etanercept is one of a new subgroup of biological disease modifying antirheumatic drugs (DMARD) to treat patients with rheumatoid arthritis (RA) who are non-responsive or intolerant to conventional DMARD. We evaluated the effects of etanercept (Enbrel) therapy in patients with RA in community-based clinical practice in Canada. METHODS: Using a cohort design, patients requesting etanercept therapy were stratified into treatment and control arms based upon their individual accessibility to obtain the drug. Patients were interviewed serially during a 12-month period of monitoring. The study measured painful or tender joint count, morning stiffness, pain severity, quality of life measures, medication utilization, health services utilization, and presence of adverse events. RESULTS: The baseline demographic and clinical variables for the treatment group (n = 223) and the control group (n = 208) were similar, except for education, income, and drug plan coverage. In followup, there was greater improvement in most clinical variables in the treatment arm compared to the control arm during the first 6 months, but the magnitude of difference between the 2 groups for some clinical variables decreased or became non-significant during the second 6 months. During the 12 month followup period there were 40 (18%) patient dropouts in the treatment group. CONCLUSION: In a community based setting for the treatment of RA, etanercept can effectively improve the disease state, functional class, work disability, and quality of life during the first 6 months of use. To determine the longterm sustainability of these effects studies with more than 12 months' duration will be 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".