Risk factors and prevalence of latent tuberculosis infection in rheumatic patients: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This study aims to evaluate the risk factors and prevalence of latent tuberculosis infection (LTBI) in patients with rheumatic diseases. METHODS: Databases including PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, Vip, and Wanfang were searched. Data extraction was performed independently by two authors. The Newcastle-Ottawa Scale (NOS) was utilized to assess study quality. Heterogeneity was evaluated using I2 statistics and the chi-square test. The relative risk (RR), odds ratio (OR) with 95% confidence intervals (95% CI), and prevalence rate were calculated. Sensitivity analysis was conducted using the leave-one-out method. Publication bias was assessed using either the Begg rank correlation or Egger’s linear regression. RESULTS: Eighteen studies (13 cross-sectional and 5 cohort studies) involving 12,167 rheumatic patients were included. Increased risk of LTBI was associated with current smoking (OR = 1.50, 95%CI: 1.28–1.78), Golimumab treatment (OR = 2.90, 95% CI: 1.08–7.78), Chloroquine treatment ( OR = 1.27, 95% CI:1.01–1.61), age > 40 (OR = 1.84, 95% CI: 1.51–2.24) and a history of tuberculosis (TB) ( OR = 3.26, 95% CI: 1.87–5.68). Additionally, male rheumatic patients had a higher risk of LTBI compared to females (OR = 1.72, 95% CI: 1.46– 2.02). However, no significant associations were found between LTBI risk and history of smoking, duration of disease, history of Bacillus Calmette-Guérin, positive rheumatoid factor, corticosteroid use, diabetes history, TB exposure, Adalimumab or Etanercept use. The pooled prevalence rate of LTBI in rheumatic patients was 22% (95% CI: 18–27%). CONCLUSIONS: Current smoking, Golimumab treatment, Chloroquine treatment, age >40 and a history of TB are identified as risk factors for LTBI in rheumatic patients. Male patients are more prone to developing LTBI. The overall LTBI prevalence in rheumatic patients is high.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.065 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".