Underestimation of Risk of Carotid Subclinical Atherosclerosis by Cardiovascular Risk Scores in Patients with Psoriatic Arthritis: How Far Are We from the Truth?
Bibliographic record
Abstract
Inflammatory disease states are associated with early cardiovascular (CV) events and are increasingly being recognized as a risk factor for developing atherosclerosis1,2. Several autoimmune conditions such as psoriasis1,3,4, systemic lupus erythematosus5, and rheumatoid arthritis (RA)6,7,8 accelerate atherosclerosis through an increase in inflammatory signaling, thus predisposing to increased CV risk. Traditional CV risk scoring systems and risk score calculation are a cornerstone in the prediction of adverse CV events9. Further, risk score calculation plays an important role in shaping treatment guidelines that are tailored to fit each patient’s individual risk factors10,11, emphasizing the need to accurately predict and stratify a patient’s CV risk. Assessment of absolute CV risk is also integral to assess major prevention and treatment targets10,11. While current scoring systems provide a modestly accurate prediction for the general population11, each scoring system has certain inherent limitations. For example, the Framingham risk score (FRS) does not incorporate socioeconomic and genetic factors (e.g., family history)12. Further, FRS does not account for systemic inflammation and, therefore, has not performed well in inflammatory diseases such as psoriasis13 and RA14. Thus, almost all the risk scoring systems do not account for systemic inflammation except the Reynold’s Risk Score, which … Address correspondence to Dr. N.N. Mehta, Section of Inflammation and Cardiometabolic Diseases, National Heart, Lung and Blood Institute, 10 Center Drive, Clinical Research Center, Room 5-5140, Bethesda, Maryland 20892, USA. E-mail: nehal.mehta{at}nih.gov
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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 | 0.044 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".