Cumulative exposure to immunomodulators increases risk of cervical neoplasia in women with inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Women with inflammatory bowel disease (IBD) are at increased risk of high-grade cervical intraepithelial neoplasia and cervical cancer (CIN2+). AIM: To assess the association between cumulative exposure to immunomodulators (IM) and biologic agents (BIO) for IBD and CIN2+ METHODS: Adult women diagnosed with IBD before December 31st 2016 in the Dutch IBD biobank with available cervical records in the nationwide cytopathology database were identified. CIN2+ incidence rates in IM- (i.e., thiopurines, methotrexate, tacrolimus and cyclosporine) and BIO- (anti-tumour necrosis factor, vedolizumab and ustekinumab) exposed patients were compared to unexposed patients and risk factors were assessed. Cumulative exposure to immunosuppressive drugs was evaluated in extended time-dependent Cox-regression models. RESULTS: The study cohort comprised 1981 women with IBD: 99 (5%) developed CIN2+ during median follow-up of 17.2 years [IQR 14.6]. In total, 1305 (66%) women were exposed to immunosuppressive drugs (IM 58%, BIO 40%, IM and BIO 33%). CIN2+ risk increased per year of exposure to IM (HR 1.16, 95% CI 1.08-1.25). No association was observed between cumulative exposure to BIO or both BIO and IM and CIN2+. In multivariate analysis, smoking (HR 2.73, 95%CI 1.77-4.37) and 5-yearly screening frequency (HR 1.74, 95% CI 1.33-2.27) were also risk factors for CIN2+ detection. CONCLUSION: Cumulative exposure to IM is associated with increased risk of CIN2+ in women with IBD. In addition to active counselling of women with IBD to participate in cervical screening programs, further assessment of the benefit of intensified screening of women with IBD on long-term IM exposure is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".