Cardiovascular Disease-related Hospital Admissions of Patients with Inflammatory Arthritis
Bibliographic record
Abstract
OBJECTIVE: Patients with inflammatory arthritis (IA) have an increased risk of cardiovascular diseases (CVD), suggesting a high rate of CVD-related hospitalizations, but data on this topic are limited. Our study addressed hospital admissions for CVD in a primary care-based population of patients with IA and controls. METHODS: All newly diagnosed patients with IA between 2001 and 2010 were selected from electronic medical records of the Netherlands Institute for Health Services Research Primary Care database, representing a national network of general practices. Two control patients matched for age, sex, and practice were selected for each patient with IA. Hospital admission data for all patients was retrieved from the Dutch Hospital Data. RESULTS: There were 2615 patients with IA and 5555 controls included in our study. CVD-related hospital admissions were observed more frequently among patients with IA as compared with control patients: 48% versus 36% (p < 0.001) in a followup period of 4 years. Patients with IA were more often hospitalized because of ischemic heart disease (OR 1.7, 95% CI 1.2-2.2) and for day-care admission because of cerebrovascular disease (OR 2.2, 95% CI 1.0-4.9). CONCLUSION: Increased hospital admission rates confirm the higher CVD burden among patients with IA compared with controls, and underscore the need for proper CVD risk management in patients with IA.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".