Screening for cervical and breast cancer among women with inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: There are limited data on rates and predictors of cervical and/or breast cancer screening among women with inflammatory bowel disease (IBD). Immunosuppressant medications used to manage IBD may increase the risk of cervical cancer precursor lesions. METHODS: Women diagnosed with IBD prior to 2002 were matched to up to 10 Manitoba residents without the disease. Logistic regression analyses were performed to compare utilization of Papanicolaou (Pap) tests and mammograms in the two groups. Among women with IBD, we analyzed the differences in screening between those with and without exposure to immunosuppressant medications and/or corticosteroids and effect of age, socioeconomic status, prior diagnostic testing, and intensity of healthcare utilization. RESULTS: In all, 54% of the 2344 women with IBD and 52% of the 15,333 controls had regular Pap tests between 2002 and 2008. In multivariate analyses, older age, lower socioeconomic status, lower intensity of healthcare utilization, Crohn's disease, and exposure to immunosuppressant medications were independent predictors of lower use of Pap testing. Although there were no significant differences in the use of mammograms in women with or without IBD or among women with IBD with or without exposure to immunosuppressant medications, only 47% of women with IBD in the target age group had mammograms regularly. CONCLUSIONS: Despite a need for higher testing, women with IBD on immunosuppressant medications are less likely to undergo Pap testing. Healthcare providers involved in the care of women with IBD need to remind these individuals about regular preventive care.
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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 | 0.000 |
| 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) | 0.002 | 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".