Risk of Rheumatoid Arthritis in Patients with Type 2 Diabetes: A Nationwide Population-Based Case-Control Study
Bibliographic record
Abstract
OBJECTIVE: Type 2 diabetes is associated with chronic, low-grade inflammation and could potentially trigger the progression of other, more prominent inflammatory diseases such as rheumatoid arthritis (RA). Therefore, we aimed to investigate the risk of incident RA in Taiwanese patients with type 2 diabetes using a population-based health claims database. METHODS: This nationwide, population-based, case-control study used administrative data to identify 1,416 patients with RA (age ≥20 years) as cases and 7,080 controls that were frequency-matched for sex, 10-year age group, and year of catastrophic illness certificate application date (index year). All subjects were retrospectively traced back, up to 13 years prior to the index year, for their first diagnosis of type 2 diabetes. Logistic regression analysis was conducted to quantify the association between incident RA and type 2 diabetes. RESULTS: The odds of developing RA were significantly higher in female (odds ratio [OR] 1.46, 95% confidence interval [95% CI] 1.24-1.72) but not in male (OR 1.00, 95% CI 0.72-1.37) patients who had previously diagnosed with type 2 diabetes. Subgroup analysis indicated that the odds of developing RA were more prominent in younger females (20 to 44 years of age) with type 2 diabetes. In addition, the odds of developing RA in female patients with type 2 diabetes were higher in those with a shorter time interval between the diagnosis of type 2 diabetes and RA. CONCLUSIONS: This large nationwide, population-based, case-control study showed an elevated risk of RA in female Taiwanese patients with type 2 diabetes. Our findings were consistent with the hypothesis that chronic low-grade inflammation in type 2 diabetes may elicit the development of RA in genetically susceptible individuals.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".