The Effect of Axial Spondyloarthritis on Mental Health: Results from the Atlas
Bibliographic record
Abstract
Objective. To assess the risk of mental disorders in patients with axial spondyloarthritis (axSpA) and to examine the factors associated with this. Methods. In 2016, a sample of 680 patients with axSpA were interviewed as part of the development process for the Atlas of Axial Spondyloarthritis in Spain. The risk of mental disorders in these patients was assessed using the 12-item General Health Questionnaire scale. Additionally, the variables associated with the risk of mental disorders were investigated, including sociodemographic characteristics (age, sex, relationship, patient association membership, job status, and educational level), disease status (Bath Ankylosing Spondylitis Disease Activity Index, spinal stiffness, and functional limitation), and previous diagnosis of mental disorders (depression and anxiety). Bivariate correlation analyses were performed, followed by multiple hierarchical and stepwise regression analysis. Results. A total of 45.6% patients were at risk of mental disorders. All variables except educational level and thoracic stiffness significantly correlated with risk of mental disorders. Nevertheless, disease activity, functional limitation, and age showed the highest coefficient (r = 0.543, p ≤ 0.001; r = 0.378, p ≤ 0.001; r = −0.174, p ≤ 0.001, respectively). In the stepwise regression analysis, 4 variables (disease activity, functional limitation, patient association membership, and cervical stiffness) explained the majority of the variance for the risk of mental disorders. Disease activity displayed the highest explanatory degree (R 2 = 0.875, p < 0.001). Conclusion. In patients with axSpA, the prevalence of risk of mental disorders is high. Combined with a certain sociodemographic profile, high disease activity is a good indicator of the risk for mental disorders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".