Females With Axial Spondyloarthritis Have Longer Diagnostic Delay and Higher Burden of the Disease. Results From the International Map of Axial Spondyloarthritis ( <scp>IMAS</scp> )
Bibliographic record
Abstract
BACKGROUND: To assess gender differences in a large sample of patients included in the International Map of Axial Spondyloarthritis (IMAS) study from around the globe. METHOD: IMAS is a cross-sectional online survey (2017-2022) of 5557 unselected axSpA patients from 27 countries. The current analysis assessed differences between males and females for: sociodemographic, health behaviors, disease characteristics, patient-reported outcomes, mental comorbidities, and treatments. Univariable and multivariable logistic regression analysis was used to evaluate the relationship between gender and disease characteristics, patient-reported outcomes, comorbidities, and treatments. RESULTS: Data from 5555 patients reporting gender were analyzed: 3492 from Europe, 769 from North America, 600 from Asia, 548 from Latin America, and 146 from Africa. Globally, 55.4% were females, with higher proportions in South Africa (82.2%) and lower in Asia (20.8%). Compared to males, a lower percentage of females smoked and consumed alcohol. The diagnostic delay was significantly longer (+2.4 years) in females, while the frequency of HLA-B27 positivity of axSpA was lower in females. The use of axSpA pharmacological treatment was more common in females with a higher proportion having ever taken nonsteroidal anti-inflammatory drugs (NSAIDs), conventional synthetic DMARDs (csDMARDs), and biologic DMARDs (bDMARDS). CONCLUSIONS: Identifying the specific disease characteristics associated with gender in patients with axSpA may help to improve the diagnosis and management of the disease, and thereby reduce the disease burden for patients around the world.
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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 | 0.002 |
| 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) | 0.004 | 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".