Understanding the Disease Burden of Unemployed Patients With Axial Spondyloarthritis: Results From the Spanish Atlas 2017
Bibliographic record
Abstract
OBJECTIVE: To evaluate differences in sociodemographic factors and patient-reported outcomes (PROs) between unemployed and employed patients with axial spondyloarthritis (axSpA), and to explore work-related issues (WRIs). METHODS: Data from an online survey of 680 unselected patients of the Atlas of Axial Spondyloarthritis in Spain 2017 were analyzed. Active workforce participants were divided into employed and unemployed groups according to International Labour Organization definitions. Sociodemographic characteristics, PROs (Bath Ankylosing Spondylitis Disease Activity Index [0-10], Spinal Stiffness Index [3-12], Functional Limitation Index [0-54], and psychological distress through the 12-item General Health Questionnaire [0-12]) were assessed. Logistic regression analysis was used to evaluate the association with unemployment status. RESULTS: Four hundred fifteen (63.6%) patients with axSpA were categorized in the active population, of which 325 (78.3%) were employed and 90 (21.7%) unemployed. Of the unemployed patients, 62.8% (n = 54) declared that their joblessness was due to axSpA. Of the employed patients, 170 (54.3%) reported WRIs in the year prior to the survey, the most frequent being difficulty fulfilling working hours (44.1%), missing work for doctor appointments (42.9%), and taking sick leave (37.1%). Being unemployed was associated with lower educational level (OR = 2.92), disease activity (OR = 1.37), spinal stiffness (OR = 1.21), functional limitation (OR = 1.05), worse mental health (OR = 1.15), anxiety (OR = 2.02), and depression (OR = 2.69) in the univariable models; and with lower educational level (OR = 2.76) and worse mental health (OR = 1.15) in the multivariable analysis. CONCLUSION: Results show significant differences between employed and unemployed patients with axSpA. Employed patients with axSpA endure many problems at work related to their condition, whereas unemployed patients present worse disease outcomes associated with greater psychological distress.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".