Cancer Incidence and Outcome for Patients With Rheumatoid Arthritis: A Long-term Population Study in Western Australia
Bibliographic record
Abstract
Objective Our aim was to compare the incidence of malignancy and its effect on mortality between hospitalized patients with rheumatoid arthritis (RA) and controls. Methods We conducted a population-level observational study of patients with RA (International Classification of Diseases, 9th revision, Clinical Modification [ICD-9-CM] code 714 and International Statistical Classification of Diseases and Related Health Problems, 10th revision, Australian Modification [ICD-10-AM] codes M05-M06) in the Hospital Morbidity Data Collection (HMDC) in Western Australia (WA) between 1985 and 2015, as well as nonexposed hospitalized controls matched on sex, age, and year of index admission. HMDC data were linked to the WA Cancer Registry and the WA Death Registry data, and cancer incidence rates (CIRs) per 1000 person-years, incidence rate ratios (IRR) with 95% CIs, and Kaplan Meier survival were estimated. Results Among 14,041 patients with RA (67.56% female, median age 65.1 years) and 33,785 controls (65.16% female, median age 65.3 years), preexisting cancer in patients with RA was less prevalent than in controls (7.6% vs 14.2%;P< 0.01). In participants without prior cancer, the overall post index CIR was lower in those with RA (CIR 19.68 vs 24.77; IRR 0.79, 95% CI 0.76-0.83) and stable over 3 study decades. CIR was higher in patients with RA for lung (CIR 1.17, 95% CI 1.04-1.34) and hematological cancer (CIR 1.21, 95% CI 1.03-1.43) but lower for most other cancer types. Overall median survival was lower for patients with RA than controls (3.3 vs 5.3 years;P< 0.001) with increased mortality rates observed for most cancer subtypes. Conclusion Overall CIR in patients with RA was consistently lower over time than in matched controls. CIR was only increased for lung and hematological cancer. Despite the overall lower CIR, post cancer mortality was higher for patients with RA in most cancer subtypes.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".