Tuberculosis in Biologic-naïve Patients With Rheumatoid Arthritis: Risk Factors and Tuberculosis Characteristics
Bibliographic record
Abstract
OBJECTIVE: To investigate risk factors and characteristics of active tuberculosis (TB) in biologic-naïve patients with rheumatoid arthritis (RA). METHODS: We conducted a population-based case-control study using the Swedish Rheumatology Quality Register, the National Patient Register, and the Tuberculosis Register to identify RA patients with active TB and matched RA controls without TB between 2001-2014. Clinical data were obtained from medical records. TB risk was estimated as adjusted OR (aOR) with 95% CI using univariate and multivariable logistic regression analyses. RESULTS: After validation of diagnoses, the study included 31 RA patients with TB and 122 matched RA controls. All except 3 cases had reactivation of latent TB. Pulmonary TB was most prevalent (84%). Ever use of methotrexate was not associated with increased TB risk (aOR 0.8, 95% CI 0.3-2.0), whereas ever treatment with leflunomide (aOR 6.0, 95% CI 1.5-24.7), azathioprine (aOR 3.8, 95% CI 1.1-13.8), and prednisolone (PSL; aOR 2.4, 95% CI 1.0-6.0) was. There were no significant differences between maximum dose of PSL, treatment duration with PSL before TB, or cumulative dose of PSL the year before TB diagnosis between cases and controls. Obstructive pulmonary disease was associated with an increased TB risk (aOR 3.9, 95% CI 1.5-10.7). CONCLUSION: Several RA-associated factors may contribute to increased TB risk in biologic-naïve patients with RA, making the risk of TB activation difficult to predict in the individual patient. To further decrease TB in patients with RA, the results suggest that screening for latent TB should also be considered in biologic-naïve patients.
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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 | 0.003 |
| 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 | 0.001 | 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".