Increased risk of mycobacterial infections associated with anti-rheumatic medications
Bibliographic record
Abstract
RATIONALE: Anti-tumour necrosis factor (TNF) agents and other anti-rheumatic medications increase the risk of TB in rheumatoid arthritis (RA). Whether they increase the risk of infections with nontuberculous mycobacteria (NTM) is uncertain. OBJECTIVES: To determine the effect of anti-TNF therapy and other anti-rheumatic drugs on the risk of NTM disease and TB in older patients with RA. METHODS: Population-based nested case-control study among Ontario seniors aged ≥67 years with RA who were prescribed at least one anti-rheumatic medication between 2001 and 2011. We identified cases of TB and NTM disease microbiologically and identified drug exposures using linked prescription drug claims. We estimated ORs using conditional logistic regression, controlling for several potential confounders. MEASUREMENTS AND MAIN RESULTS: Among 56 269 older adults with RA, we identified 37 cases of TB and 211 cases of NTM disease; each case was matched to up to 10 controls. Individuals with TB or NTM disease were both more likely to be using anti-TNF therapy (compared with non-use); adjusted ORs (95% CIs) were 5.04 (1.27 to 20.0) and 2.19 (1.10 to 4.37), respectively. Exposure to leflunomide and other anti-rheumatic drugs with high immunosuppressing potential also were associated with both TB and NTM disease, while oral corticosteroids and hydroxychloroquine were associated with NTM disease. CONCLUSIONS: Anti-TNF use is associated with increased risk of both TB and NTM disease, but appears to be a relatively greater risk for TB. Several other anti-rheumatic drugs were also associated with mycobacterial infections.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".