Value of Carotid Ultrasound in Cardiovascular Risk Stratification in Patients With Psoriatic Disease
Bibliographic record
Abstract
OBJECTIVE: This study aimed to assess whether subclinical atherosclerosis, as evaluated by carotid ultrasound, could predict incident cardiovascular events (CVEs) in patients with psoriatic disease (PsD) and determine whether incorporation of imaging data could improve CV risk prediction by the Framingham Risk Score (FRS). METHODS: In this cohort analysis, patients with PsD underwent ultrasound assessment of the carotid arteries at baseline. The extent of atherosclerosis was assessed using carotid intima-media thickness (CIMT) and total plaque area (TPA). Incident CVEs (new or recurrent) that occurred following the ultrasound assessment were identified. The association between measures of carotid atherosclerosis and the risk of developing an incident CVE was evaluated using Cox proportional hazards models, with adjustment for the FRS. RESULTS: In total, 559 patients with PsD were assessed, of whom 23 had incident CVEs ascertained. The calculated rate of developing a first CVE during the study period was 1.11 events per 100 patient-years (95% confidence interval [95% CI] 0.74-1.67). When analyzed separately in Cox proportional hazards models that were controlled for the FRS, the TPA (hazard ratio [HR] 3.74, 95% CI 1.55-8.85; P = 0.003), mean CIMT (HR 1.21, 95% CI 1.03-1.42; P = 0.02), maximal CIMT (HR 1.11, 95% CI 1.01-1.22; P = 0.03), and high TPA category (HR 3.25, 95% CI 1.18-8.95; P = 0.02) were each predictive of incident CVEs in patients with PsD. CONCLUSION: The burden of carotid atherosclerosis is associated with an increased risk of developing future CVEs. Combining vascular imaging data with information on traditional CV risk factors could improve the accuracy of CV risk stratification in patients with PsD.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".