Risk of Diabetes in Patients with Rheumatoid Arthritis: A 12-year Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The incidence of type 2 diabetes (T2D) in adults with rheumatoid arthritis (RA) was investigated, and the results were compared with non-RA controls to confirm whether RA is a risk factor for diabetes mellitus (DM) in Taiwan. METHODS: We used a databank of 1 million individuals randomly selected from 23 million Taiwanese citizens covered by the National Health Insurance plan in 2005. All persons older than age 20 years in 1998 and not diagnosed with either RA or T2D before 1998 were included. They were divided into 2 cohorts, 1 with RA and the other without. Those who had T2D before RA were excluded. Each patient in the RA cohort was followed from the RA diagnosis until the end of 2009, or until dropping out of the insurance coverage. RA was ascertained by at least 3 visits using ICD-9 code 714.0, plus at least 2 visits with prescription of antirheumatic drugs in a period of 12 months. T2D was ascertained by at least 3 visits with diabetes codes within 1 year, while hypertension (HTN) and disorders of lipid metabolism (DLM) were determined by at least 3 visits using corresponding ICD codes during the study period. Kaplan-Meier plots, log-rank tests, and Cox regression were used to study the effects of age, sex, glucocorticoid use, HTN, DLM, and RA on T2D risk. RESULTS: The subjects include 600,695 adults. Of these, 4193 were diagnosed with RA, and among them 799 were diagnosed with T2D. The RA to non-RA risk ratio for T2D was 1.68 (95% CI 1.53-1.84) in men and 1.46 (95% CI 1.39-1.54) in women. CONCLUSION: RA appears to be associated with an increased risk for T2D in Taiwan.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".