Disease Progression and Treatment Responses in a Prospective DMARD-naive Seropositive Early Rheumatoid Arthritis Cohort: Does Gender Matter?
Bibliographic record
Abstract
OBJECTIVE: To assess gender differences in disease characteristics and treatment responses over time in a disease-modifying antirheumatic drug (DMARD)-naive seropositive early rheumatoid arthritis (RA) cohort. METHODS: Patients with polyarticular disease who were DMARD-naive and had seropositive early RA (< 14 months) were recruited by the Western Consortium of Practicing Rheumatologists. Each patient was examined at study entry, after 6 and 12 months, and yearly thereafter. Clinical and demographic data were collected. We investigated gender differences in baseline disease characteristics and treatment using chi-squared, Mann-Whitney U, and t tests. We used generalized estimating equations (GEE) models for repeated measures to examine whether the rate of change of specific disease outcomes during the first 2 years after DMARD initiation was significantly influenced by gender. RESULTS: At baseline, men (n = 67) and women (n = 225) had similar disease activity and radiographic damage; men, however, had significantly worse erosion, while women had worse joint space narrowing. Despite similar treatment, women had worse disease progression over the 2-year followup, as assessed by trends in Disease Activity Score 28/erythrocyte sedimentation rate (DAS28-ESR4), physician global scores, and tender joint counts. In the GEE model, gender was significantly associated with the rate of change of DAS28-ESR4 scores (p = 0.009), although not independently associated with disease activity. Self-reported measures (Health Assessment Questionnaire-Disability Index, patient global scores, fatigue, pain) were worse among women at baseline and throughout the study period. Men were more likely to achieve remission. CONCLUSION: At baseline, men and women had similar disease activity and joint damage. Responses to treatment over time were better among men in this prebiologic era; women had worse progression despite similar 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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".