Association Between Immunoglobulin G4–related Disease and Malignancy within 12 Years after Diagnosis: An Analysis after Longterm Followup
Bibliographic record
Abstract
OBJECTIVE: Because it is uncertain whether immunoglobulin G4-related disease (IgG4-RD) is associated with malignancy, we evaluated the incidence of cancer development in a large cohort of patients with IgG4-RD. METHODS: The study enrolled 158 patients diagnosed as having IgG4-RD between 1992 and 2012. We calculated the standardized incidence ratio (SIR) and cumulative rate of malignancies in this group and searched for risk factors associated with the occurrence of tumors. RESULTS: A total of 34 malignancies were observed in the patients with IgG4-RD over a mean followup period of 5.95 ± 4.48 years. The overall SIR of malignancies was 2.01 (95% CI 1.34-2.69). The SIR of patients who exhibited a tumor within 1 year after IgG4-RD diagnosis was 3.53 (95% CI 1.23-5.83), while that of subjects forming a malignancy in subsequent years was 1.48 (95% CI 0.99-1.98). The cumulative rate of malignancy development was significantly higher in patients with IgG4-RD within 12 years after diagnosis than in the Japanese general population. Comparable results were obtained for an autoimmune pancreatitis subgroup. The serum concentrations of several disease activity markers at diagnosis were significantly higher in patients with malignancies than in those without. CONCLUSION: We identified a close association between IgG4-RD and malignancy formation within 12 years after diagnosis, particularly during the first year. An active IgG4-RD state is presumed to be a strong risk factor for malignancy development.
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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 | 0.001 |
| 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 | 0.001 | 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".