Predicting the Risk of Ischemic Stroke among Patients with Rheumatoid Arthritis Using a Simplified RA-CHADSV Score Based on the CHA2DS2-VASc Score
Bibliographic record
Abstract
Background and Objectives: The aim of this retrospective cohort study was to develop a new score (RA-CHADSV) (rheumatoid arthritis - congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke/transient ischemic attack/thromboembolism, and vascular disease), modified from the CHA2DS2-VASc score (congestive heart failure, hypertension, age ≥75 years (doubled), diabetes mellitus, stroke/transient ischemic attack (doubled), vascular disease, age 65–74 years, and female), in predicting the risk of ischemic stroke in rheumatoid arthritis (RA) patients without atrial fibrillation (AF). Materials and Methods: Using the Taiwan’s National Health Insurance Research Database, 592 patients with RA diagnosed between 2000 and 2002 were identified and followed until first occurrence of ischemic stroke or the last available date in the database. Incidence rate ratios (IRR) of ischemic stroke for the CHA2DS2-VASc score were calculated using Poisson regression models. A new prediction score RA-CHADSV was developed using multiple logistic regression analysis with bootstrap validation. Results: The area under the receiver operating characteristic curve of the newly developed RA-CHADSV score and the CHA2DS2-VASc score were 0.73 (95% confidence interval (CI) 0.64–0.82) and 0.70 (95% CI 0.61–0.79), respectively. The RA-CHADSV score was significantly associated with a higher ischemic stroke incidence in the patients who scored ≥1 (adjusted IRR 7.39, p < 0.001). Conclusions: A simplified RA-CHADSV score, with comparable efficiency as the CHA2DS2-VASc score, but easier to use clinically was developed for predicting the risk of ischemic stroke among non-AF RA patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".