Prevalence and risk factors of active tuberculosis in patients with rheumatic diseases: a multi-center, cross-sectional study in China
Bibliographic record
Abstract
Evidence of active tuberculosis (ATB) in patients with rheumatic diseases are research priorities but limited data from China have been reported. Research targeting patients not taking anti-TNF biologics are especially insufficient. We aimed to investigate the prevalence and risk factors of ATB in this at-risk population. We conducted a tertiary hospital-based, multi-center, cross-sectional study by using stratified multi-stage cluster sampling strategy to screen ATB in patients with rheumatic diseases. We estimated the prevalence of ATB in patients with rheumatic diseases and identified risk factors among those who were not taking anti-TNF biologic. A total of 13,550 eligible patients were enrolled, and the result showed the standardized prevalence of ATB according to the composition ratio of various types of rheumatic disease was 882/100000 (95% confidence interval (CI): 706-1057). Multivariable logistic regression analysis in patients not taking anti-TNF biologics showed that the independent risk factors of ATB were having systemic lupus erythematosus (SLE) (OR=2.722, 95% CI: 1.437-5.159, p=0.002), having Behcet’s disease (BD) (OR= 5.261, 95% CI: 2.071-13.365, p<0.001), taking azathioprine(AZA) within the past two years (OR=2.095, 95% CI: 0.986-4.450, p=0.054), exposing to glucocorticoids ≥30mg/d for more than four weeks within the past two years (OR=2.031, 95% CI: 1.247-3.309, p=0.004) and having evidences of previous TB (OR= 6.185, 95% CI: 3.487-10.969, p<0.001). The prevalence of ATB was higher in patients with rheumatic diseases compared to the general population. Patients with SLE or BD, prolonged exposure to moderate to high dose of glucocorticoids and previous TB were independent risk factors for ATB.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".