Overall and Comparative Risk of Shingles With Advanced Therapies in Patients With Inflammatory Bowel Diseases
Bibliographic record
Abstract
BACKGROUND: Advanced therapies increase the risk of shingles in inflammatory bowel diseases (IBD). AIM: To compare the risk of shingles with different advanced therapies in patients with IBD. METHODS: We identified patients with IBD who initiated treatment with tumour necrosis factor (TNF) antagonists, anti-integrins, anti-interleukins, Janus kinase (JAK) inhibitors, or sphingosine-1 phosphate receptor (S1PR) modulators between 2016 and 2022 and had follow-up for at least 1 year before and after treatment initiation. We estimated the incidence rate (IR per 100 person-year [PY]) of shingles (overall and complicated) and compared the risk with different advanced therapies through multinomial propensity score-based inverse probability of treatment weighting (IPTW), with propensity scores estimated through generalised boosted models, accounting for disease characteristics, healthcare utilisation, comorbidities and prior and concomitant medications. Weighted Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for multiple treatment comparisons. RESULTS: We included 21,675 patients followed over 27 months. IRs per 100 PY of shingles and complicated shingles were: TNF antagonists 1.0/0.3, anti-integrin agents 1.4/0.4, anti-interleukins 1.2/0.5, JAK inhibitors 3.3/0.9 and S1PR modulators 2.0/1.0. After adjusting for confounding variables, JAK inhibitors were associated with higher risks of shingles than TNF antagonists (HR 2.19; 1.28-3.75), anti-integrins (HR 1.87; 1.12-3.14) and anti-interleukins (HR 1.70; 0.98-2.96). CONCLUSIONS: JAK inhibitors were associated with 1.7-2.2-fold higher risk of shingles than other advanced therapies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".