Determinants Associated with Work Participation in Patients with Established Rheumatoid Arthritis Taking Tumor Necrosis Factor Inhibitors
Bibliographic record
Abstract
OBJECTIVE: Reduced work participation (WP) is a common problem for patients with rheumatoid arthritis (RA) and generates high costs for society. Therefore, it is important to explore determinants of WP at the start of tumor necrosis factor inhibitor (TNFi) treatment, and for changes in WP after 2 years of TNFi treatment. METHODS: Within the Dutch Rheumatoid Arthritis Monitoring (DREAM) biologic register, WP data were available from 508 patients with RA younger than 65 years and without an (early) retirement pension. WP was registered at start of TNFi treatment and after 2 years of followup and was measured by single patient-reported binary questions whether they had work, paid or voluntary, or had a disability allowance or a retirement pension. Determinants measured at baseline were age, sex, disease duration, functional status [through Health Assessment Questionnaire-Disability Index (HAQ-DI)], 28-joint Disease Activity Score (DAS28), rheumatoid factor, presence of erosions, number of previous disease-modifying antirheumatic drugs, and number of comorbidities. During the 2 years of followup, HAQ-DI response and European League Against Rheumatism response were measured. Univariate analyses (excluded if p value was > 0.2) and multivariate (excluded if p value was > 0.1) logistic regression analyses were used. RESULTS: Determinants associated with WP at baseline were having a better HAQ-DI (OR 0.32, p = 0.000) and male sex (OR 0.65, p = 0.065). After 2 years of TNFi therapy, 11.8% (n = 60) started to work and 13.6% (n = 69) stopped working. Determinants associated with starting to work were better baseline HAQ-DI (OR 0.58), positive RF (OR 2.73), and young age (OR 0.96); and for stopping work, worse baseline HAQ-DI (OR 2.74), low HAQ-DI response (OR 0.31), and comorbidity (OR 2.67), all with p < 0.1. CONCLUSION: Young patients with RA and a high functional status without any comorbidity will have a better chance of working. This supports the main goal in the management of RA: to suppress disease activity as soon and as completely as possible to prevent irreversible destruction of the joints, and thus maintain a good functional status of the patient. Because of the low proportion of variance explained by the models in this study, other factors besides the ones studied are associated with WP.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".