Performance of the Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis Is Not Superior to the ACC/AHA Risk Calculator
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular (CV) risk estimation calculators for the general population do not perform well in patients with rheumatoid arthritis (RA). An RA-specific risk calculator has been developed, but did not perform better than a risk calculator for the general population when validated in a heterogeneous multinational cohort. METHODS: In a cohort of patients with new-onset RA from northern Sweden (n = 665), the risk of CV disease was estimated by the Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis (ERS-RA) and the American College of Cardiology/American Heart Association algorithm (ACC/AHA). The ACC/AHA estimation was analyzed, both as crude data and when adjusted according to the recommendations by the European League Against Rheumatism (ACC/AHA × 1.5). ERS-RA was calculated using 2 variants: 1 from patient and physician reports of hypertension (HTN) and hyperlipidemia [ERS-RA (reported)] and 1 from assessments of blood pressure (BP) and blood lipids [ERS-RA (measured)]. The estimations were compared with observed CV events. RESULTS: All variants of risk calculators underestimated the CV risk. Discrimination was good for all risk calculators studied. Performance of all risk calculators was poorer in patients with a high grade of inflammation, whereas ACC/AHA × 1.5 performed best in the high-inflammatory patients. In those patients with an estimated risk of 5-15%, no risk calculator performed well. CONCLUSION: ERS-RA underestimated the risk of a CV event in our cohort of patients, especially when risk estimations were based on patient or physician reports of HTN and hyperlipidemia instead of assessment of BP and blood lipids. The performance of ERS-RA was no better than that of ACC/AHA × 1.5, and neither performed well in high-inflammatory patients.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".