Risk of Cancer Among Sarcoidosis Patients With Biopsy-verified Nonnecrotizing Granulomatous Inflammation: Population-based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To assess the long-term risk of hematologic cancers, invasive solid tumors, and nonmelanoma skin cancer (NMSC) among sarcoidosis patients with biopsy-verified nonnecrotizing granulomatous inflammation. METHODS: We used Danish administrative registers with nationwide coverage to construct a cohort of 3892 patients with sarcoidosis and an age- and sex-matched comparison cohort of 38,920 population controls. For all patients, a biopsy demonstrating nonnecrotizing granulomatous inflammation had been obtained from the lower respiratory tract at the time of diagnosis. Study outcome was time to diagnosis of cancer. Follow-up began at time of sarcoidosis diagnosis and continued for up to 10 years. We calculated hazard ratios (HRs) as estimates of the cancer risk among the patients with sarcoidosis relative to that among the population controls and used cumulative incidence functions to calculate absolute 10-year risk estimates. RESULTS: We observed an increased long-term risk of hematologic cancers (HR during the first 2 years of follow-up: 2.71 [95% CI 1.18-6.25]; HR after > 2 years of follow-up: 2.12 [95% CI 1.29-3.47]) and NMSC (HR after > 2 years of follow-up: 1.82 [95% CI 1.43-2.32]) among the patients with sarcoidosis. An increased risk of invasive solid tumors was only observed during the first 2 years (HR 1.55, 95% CI 1.18-2.04). Compared with the population controls, the patients with sarcoidosis had an increased absolute 10-year risk of hematologic cancers (risk difference 0.56%, 95% CI 0.11-1.01%) and NMSC (risk difference 1.58%, 95% CI 0.70-2.47%). CONCLUSION: Sarcoidosis patients with biopsy-verified nonnecrotizing granulomatous inflammation have an increased long-term risk of hematologic cancers and NMSC compared with the general population.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".