Risk of Overall and Site-specific Cancers in Behçet Disease: A Nationwide Population-based Study in Taiwan
Bibliographic record
Abstract
OBJECTIVE: The relationship between autoimmune disease and cancer is complex while large-scale epidemiological studies of cancer risk in Behçet disease (BD) have not been reported. Therefore, we conducted a nationwide population-based cohort study. METHODS: By using the National Health Insurance Research Database of 23 million people in Taiwan, we identified 1314 new patients with BD without previous cancer from 2000-2009 as a cohort. Standardized incidence ratios (SIR) of overall and site-specific cancers in patients with BD in comparison with the general population were calculated from 2000-2011. RESULTS: Among the 1314 patients with BD, 30 developed cancers (9 men and 21 women). In overall cancer risk analysis, patients with BD had a higher risk (SIR 1.5, 95% CI 1.03-2.11). Among them, female patients with BD (SIR 1.8, 95% CI 1.14-2.7), but not male patients with BD (SIR 1.08, 95% CI 0.53-1.98), have a higher risk of overall cancer. In site-specific cancer risk analysis, patients with BD had a higher risk of non-Hodgkin lymphoma (SIR 8.3, 95% CI 2.1-22.7), hematological malignancy (SIR 4.2, 95% CI 1.3-10.2), and female breast cancer (SIR 2.2, 95% CI 1.004-4.1). The cancer risk was highest within the first-year followup (SIR 2.7, 95% CI 1.3-5.1), with 75% of the hematological malignancies found within the first year. CONCLUSION: This nationwide cohort study of cancer risk in patients with BD provides important information about the relationship between BD and malignancies. The results can be useful for cancer surveys in the future.
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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 | 0.000 | 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".