Sex Differences in Response to Anti-Tumor Necrosis Factor Therapy in Early and Established Rheumatoid Arthritis — Results from the DANBIO Registry
Bibliographic record
Abstract
OBJECTIVE: To investigate sex differences in response to anti-tumor necrosis factor-α (TNF-α) therapy over time in early versus established rheumatoid arthritis (RA). METHODS: Patients with RA who initiated anti-TNF therapy between January 2003 and June 2008 in Denmark were selected from the DANBIO Registry. Sex differences in baseline disease features were examined using chi-square, Mann-Whitney U tests, and t tests. Using a generalized estimating equations (GEE) model for repeated measures, we examined European League Against Rheumatism (EULAR) responses in men and women over 48 months of followup, adjusting for baseline values of age, 28-joint Disease Activity Score (DAS28), disease duration, and anti-TNF, methotrexate, and prednisolone use. RESULTS: At initiation of anti-TNF therapy (baseline), 328 women and 148 men had early RA (≤ 2 yrs), and 1245 women and 408 men had established RA (> 2 yrs). In both early and established RA, men and women had active disease with similar DAS28 scores (mean ± SD 5.2 ± 1.1), physician global scores, swollen joint counts, and radiographic changes. In early RA, men were significantly more likely to achieve a EULAR good/moderate response over 48 months compared to women (GEE; p = 0.003), and a significant interaction between sex and followup time (GEE; p < 0.0005) suggested that men achieved this response sooner than women. CONCLUSION: Better responses to anti-TNF therapy among men compared to women in early but not established RA suggest that disease duration at initiation of therapy may be an important factor to consider when investigating sex differences in treatment responses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".