Evaluation of the Effect of Diabetes on Rheumatoid Arthritis–related Outcomes in an Electronic Health Record–based Rheumatology Registry
Bibliographic record
Abstract
OBJECTIVE: Patients with rheumatoid arthritis (RA) who also have diabetes mellitus (DM) might have worse clinical outcomes and adverse events compared to patients with RA who do not have DM. We evaluated the effects of DM on Health Assessment Questionnaire (HAQ) changes and outpatient infection rates in patients with RA. METHODS: Using the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) electronic health record-based registry, we identified patients with RA who had ≥ 1 rheumatologist visit with a HAQ measured in 2016 (index visit), ≥ 1 previous visit, and a subsequent outcome visit with the same HAQ measured at 12 months (± 3 months). We identified DM by diagnosis codes, medications, or laboratory values. Outpatient infection was defined by diagnosis codes or antiinfective medications. We calculated mean HAQ change and incidence rate (IR) of outpatient infections among patients with and without DM. Generalized linear models and Cox regression were used to calculate the adjusted mean HAQ change and HRs. RESULTS: < 0.01). We identified 761 outpatient infections for patients with DM with an IR of 22.6 (95% CI 21.0-24.2) per 100 person-years and 3239 among patients without DM with an IR of 19.8 (95% CI 19.1-20.5). The adjusted HR of outpatient infections among patients with DM was 0.99 (95% CI 0.91-1.07), compared to patients without DM. CONCLUSION: Patients with RA with concomitant DM had greater worsening, or less improvement, in their functional status, suggesting additional interventions may be needed for RA patients with DM to optimize treatment and management of other comorbidities.
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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 | 0.036 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".