Anti-TNF Therapy and the Risk of Herpes Zoster Among Patients With Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: The specific contribution of anti-TNF therapy to the onset of herpes zoster (HZ) in patients with inflammatory bowel disease (IBD) remains uncertain. Thus, the purpose of this nested case-control study was to explore whether the use of anti-TNF therapy is associated with an increased risk of HZ. METHODS: Using the Regie de l'Assurance Maladie du Québec, we identified incident cases of IBD between 1998 and 2015. We matched IBD cases of HZ with up to 10 IBD HZ-free controls on year of cohort entry and follow-up. Current use was defined as a prescription for anti-TNF therapy 60 days before the index date, with nonuse as the comparator. We conducted conditional logistic regression to estimate odds ratios (ORs) with 95% confidence intervals (CIs), adjusting for potential confounders. RESULTS: The cohort consisted of 15,454 incident IBD patients. Over an average follow-up of 5.0 years, 824 patients were diagnosed with HZ (incidence of 9.3 per 1000 person-years). Relative to nonuse, current use of anti-TNF therapy was associated with an overall increased risk of HZ (OR, 1.5; 95% CI, 1.1-2.1). The risk was increased among those older than 50 years (OR, 2.1; 95% CI, 1.2-3.6) and those additionally using steroids and immunosuppressants (OR, 4.1; 95% CI, 2.3-7.2). CONCLUSIONS: Use of anti-TNF therapy was associated with an increased risk of HZ among patients with IBD, particularly among those older than 50 years and those on combination therapy. Prevention strategies for HZ ought to be considered for younger IBD patients commencing treatment.
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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 | 0.004 |
| 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 | 0.001 | 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 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".