Achieving inactive disease state in men and women with axial spondyloarthritis: results from a multi-country prospective observational study
Bibliographic record
Abstract
OBJECTIVE: To assess the probability of achieving low disease activity (LDA) and inactive disease (ID) in men vs women with recently diagnosed axial spondyloarthritis (axSpA). METHODS: This 1-year post-hoc analysis of the PROOF study assessed patients with recently diagnosed (≤12 months) axSpA. Patients were classified as having radiographic (r) or non-radiographic (nr) axSpA using central radiograph readings. Demographics, disease characteristics, and treatment were assessed at baseline and annually thereafter. Disease activity at 1 year was assessed by Axial Spondyloarthritis Disease Activity Score (ASDAS). RESULTS: 1385 patients with baseline and year 1 data were included. Patients were classified as having nr-axSpA (n = 477 [34%]; 47% men) or r-axSpA (n = 908 [66%]; 71% men). Men had higher TNF inhibitor (TNFi) use vs women with nr-axSpA (15% vs 9%; P = 0.0238) and r-axSpA (20% vs 13%; P = 0.0118). At 1 year, TNFi use increased, remaining significantly higher for men with r-axSpA (34.9% vs 28.1%; P = 0.0497) but not nr-axSpA. Significantly more men with nr-axSpA achieved ASDAS LDA (46.0% vs 27.1%; P = 0.0002) and ID (21.7% vs 9.6%; P = 0.0013); no significant difference was observed in r-axSpA. Female sex was associated with significantly lower odds of achieving LDA and ID in nr-axSpA only (odds ratio, men vs women, LDA: 2.23 [95% CI, 1.45, 3.42]; ID: 2.40 [1.39, 4.12]). CONCLUSION: Women with nr-axSpA, but not r-axSpA, were less likely to achieve LDA and ID after 1 year than men. These data stress the need to carefully evaluate the diagnosis and causes of pain in patients diagnosed with axSpA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".