Systemic Sclerosis and the Risk of Tuberculosis
Bibliographic record
Abstract
OBJECTIVE: Pulmonary involvement is common in patients with systemic sclerosis (SSc), and this condition causes substantial morbidity and mortality. Disrupted immunity from the disease or associated medication may render such patients subject to tuberculosis (TB) infection. However, the relationship between SSc and TB has not yet been investigated. METHODS: Using the Taiwan National Health Insurance Research Database, 838 patients with SSc diagnosed in Taiwan during 2000-2006 were identified and followed for emergence of TB infection. Incidence rate ratios (IRR) of TB compared to 8380 randomly selected age-, sex-, and comorbidity-matched controls without SSc were calculated. The Cox proportional hazards model was used for multivariate adjustment to identify independent risk factors for TB infection. RESULTS: The risk of TB infection was higher in the SSc cohort than in controls (IRR 2.81, 95% CI 1.36-5.37; p = 0.004), particularly for pulmonary TB (IRR 2.53, 95% CI 1.08-5.30; p = 0.022). Other independent risk factors for TB infection in patients with SSc were age ≥ 60 years [hazard ratio (HR) 3.52, 95% CI 1.10-11.33; p = 0.035] and pulmonary hypertension (PH; HR 6.06, 95% CI 1.59-23.17; p = 0.008). Mortality did not differ for SSc patients with or without TB. CONCLUSION: In this nationwide study, the incidence of TB infection was significantly higher among patients with SSc than in controls without SSc. Special care should be taken in managing patients with SSc who are at high risk for TB, especially those aged ≥ 60 years or who also have PH.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".