Disease Activity Influences Cardiovascular Risk Reclassification Based on Carotid Ultrasound in Patients with Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Because the addition of carotid ultrasound (US) into composite cardiovascular (CV) risk scores has been found effective for identifying patients with inflammatory arthritis and high CV risk, we aimed to determine whether its use would facilitate the reclassification of patients with psoriatic arthritis (PsA) into the very high Systematic Coronary Risk Evaluation (SCORE) risk category and whether this might be related to disease features. METHODS: This was a cross-sectional study involving 206 patients who fulfilled ClASsification for Psoriatic ARthritis criteria for PsA, and 179 controls. We assessed lipid profile, SCORE, disease activity measurements, and the presence of carotid plaques and carotid intima-media thickness by ultrasonography. A multivariable regression analysis, adjusted for classic CV risk factors, was performed to evaluate whether the risk of reclassification could be explained by disease-related features and to assess the most parsimonious combination of risk reclassification predictors. RESULTS: Forty-seven percent of patients were reclassified into a very high SCORE risk category after carotid US compared to 26% of controls (p < 0.001). Patients included in the low SCORE risk category were those who were more commonly reclassified (30% vs 14%, p = 0.002). The Disease Activity Index for PsA (DAPSA) score was associated with reclassification (β 1.10, 95% CI 1.02-1.19; p = 0.019) after adjusting for age and traditional CV risk factors. A model containing SCORE plus age, statin use, and DAPSA score yielded the highest discriminatory accuracy compared to the SCORE-alone model (area under the receiver-operating characteristic curve 0.863, 95% CI 0.789-0.936 vs 0.716, 95% CI 0.668-0.764; p < 0.001). CONCLUSION: Patients with PsA are more frequently reclassified into the very high SCORE risk category following carotid US assessment than controls. This was independently explained by the disease activity.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".