Characteristics Predicting Tuberculosis Risk under Tumor Necrosis Factor-α Inhibitors: Report from a Large Multicenter Cohort with High Background Prevalence
Bibliographic record
Abstract
OBJECTIVE: Screening strategies for latent tuberculosis (TB) before starting tumor necrosis factor (TNF)-α inhibitors have decreased the prevalence of TB among patients who are treated with these agents. However, despite vigilant screening, TB continues to be an important problem, especially in parts of the world with a high background TB prevalence. The aim of this study was to determine the factors related to TB among a large multicenter cohort of patients who were treated with anti-TNF. METHODS: Fifteen rheumatology centers participated in this study. Among the 10,434 patients who were treated with anti-TNF between September 2002 and September 2012, 73 (0.69%) had developed TB. We described the demographic features and disease characteristics of these 73 patients and compared them to 7695 patients who were treated with anti-TNF, did not develop TB, and had complete data available. RESULTS: Among the 73 patients diagnosed with TB (39 men, 34 women, mean age 43.6 ± 13 yrs), the most frequent diagnoses were ankylosing spondylitis (n = 38) and rheumatoid arthritis (n = 25). More than half of the patients had extrapulmonary TB (39/73, 53%). Six patients died (8.2%). In the logistic regression model, types of anti-TNF drugs [infliximab (IFX), OR 3.4, 95% CI 1.88-6.10, p = 0.001] and insufficient and irregular isoniazid use (< 9 mos; OR 3.15, 95% CI 1.43-6.9, p = 0.004) were independent predictors of TB development. CONCLUSION: Our results suggest that TB is an important complication of anti-TNF therapies in Turkey. TB chemoprophylaxis less than 9 months and the use of IFX therapy were independent risk factors for TB development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".