Cancer Risk and Mortality in Hospitalized Patients With Idiopathic Inflammatory Myopathies in Western Australia
Bibliographic record
Abstract
Objective To compare cancer incidence, type, and survival between patients with idiopathic inflammatory myopathies (IIMs) in Western Australia (WA) and the general population. Methods Administrative health data for hospitalized patients with incident IIM (n = 803, 56.5% female, median age 62.0 yrs), classified by a validated algorithm as polymyositis (PM; 36.2%), dermatomyositis (DM; 27.4%), inclusion body myositis (IBM; 17.1%), overlap myositis (OM; 10.7%), and other IIM (8.6%), were linked to WA cancer and death registries for the period of 1980 to 2014. Cancer incidence rates (CIRs) before and after IIM diagnosis as well as cancer mortality were compared with age-, sex-, and calendar year–matched controls (n = 3225, 54.9% female, median age 64 yrs) by rate ratios (RRs) and Kaplan-Meier survival estimates. Results The prediagnosis CIR was similar for patients with IIM and controls (6.57 vs 5.95; RR 1.11, 95% CI 0.88-1.39) and for patients evolving to DM (n = 220) or other IIM subtypes (6.59 vs 6.56; RR 1.01, 95% CI 0.38-3.69). During follow-up, CIR was higher for all DM (4.05, 95% CI 3.04-5.29), with increased CIR for lung cancer vs controls (1.05 vs 0.33; RR 3.18, 95% CI 1.71-5.47). Cancer post diagnosis shortened life span by 59 months for patients with IIM (103 vs 162 months,P< 0.01), but reduced survival rates were observed only in patients with DM and IBM. Conclusion Cancer risk was not increased prior to IIM, but CIR for lung cancer was increased following DM diagnosis. As cancer reduced survival only in patients with DM and IBM, these data support a strategy of limited cancer screening in IIM.
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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 | 0.002 |
| 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 | 0.001 |
| 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".