Cumulative Incidence of Cancer Screening for Breast, Cervical, Prostate, and Colorectal Cancer in Patients With Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of breast, cervical, prostate, and colorectal cancer screening in patients with rheumatoid arthritis (RA) vs matched non-RA comparators. METHODS: We performed a retrospective, matched cohort study of patients with and without RA living in an 8-county region of southern Minnesota on January 1, 2015. Through review of medical records, patients who fulfilled either the 1987 American College of Rheumatology (ACR) or 2010 ACR/European Alliance of Associations for Rheumatology classification criteria for RA were identified. Patients with RA were matched 1:1 to non-RA comparators on age, sex, and county of residence. Cancer screening was determined from review of the US Preventative Task Force recommendations. Cumulative incidence of cancer screening was estimated accounting for the competing risk of death, and Cox proportional hazard models adjusted for age, smoking, and race assessed for the risk of delay. RESULTS: The study included 1614 patients with RA and 1597 comparators without RA (mean age 63 years, 71% female). At 5-years of follow-up, 51.6% (95% CI 47.9-55.6) of the RA cohort had cervical cancer screening compared to 58.2% (95% CI 54.5-62.2) in the non-RA cohort. After adjusting for age, smoking, and race, RA was associated with decreased cervical cancer screening (adjusted hazard ratio [aHR] 0.83, 95% CI 0.72-0.96). RA was not significantly associated with a decrease in breast (aHR 0.98, 95% CI 0.87-1.10), prostate (aHR 0.99, 95% CI 0.74-1.34), or colorectal (aHR 1.04, 95% CI 0.93-1.16) cancer screening. CONCLUSION: Women with RA were more likely to experience delayed cervical cancer screening. Increased diligence by healthcare providers to ensure cervical cancer screening in patients with RA is important to reduce the morbidity and mortality seen in these patients.
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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 | 0.002 |
| 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) | 0.001 | 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".