Incidence of Tuberculosis Among Korean Patients with Ankylosing Spondylitis Who Are Taking Tumor Necrosis Factor Blockers
Bibliographic record
Abstract
OBJECTIVE: To assess the incidence and relative risk of new tuberculosis (TB) infections in Korean patients with ankylosing spondylitis (AS) and patients with AS who are undergoing treatment with tumor necrosis factor (TNF) blockers. METHODS: New cases of TB were identified by reviewing the medical records of 919 patients with AS not treated with TNF blockers and those of 354 patients with AS treated with adalimumab (n = 66), infliximab (n = 78), or etanercept (n = 210) between 2002 and 2009. Reference data were obtained from the Korean National Tuberculosis Association. RESULTS: The mean incidence rate of TB was 69.8 per 100,000 person-years (PY) in the general population, 308 per 100,000 PY in the TNF blocker-naive AS cohort, and 561 per 100,000 PY in the TNF blocker-exposed AS cohort. The incidence rate of TB in the infliximab-treated AS cohort (540 per 100,000 PY) was higher than that in the adalimumab-treated AS cohort (490 per 100,000 PY). No cases of TB occurred in the etanercept-treated AS cohort. Comparing the relative risks of TB infections between the TNF blocker-exposed AS cohort and the TNF blocker-naive AS cohort, no statistically significant difference was identified (risk ratio 0.53; 95% CI 0.144-1.913). CONCLUSION: The risk of TB was higher in the TNF blocker-naive AS cohort than it was in the general population. However, the risk of TB was not increased in the TNF blocker-exposed AS cohort compared with the TNF blocker-naive AS cohort. Among patients with AS, etanercept is associated with a lower risk of TB compared with monoclonal antibodies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".