How to Manage Cardiovascular Disease in Psoriatic Disease: Evidence and Time Management in Clinic
Bibliographic record
Abstract
Early recognition and appropriate management of cardiovascular (CV) disease (CVD) in patients with psoriatic disease (PsD) is critical for prevention of early morbidity and mortality. PsD and CVD share common pathogenic mechanisms, including upregulation of proinflammatory cytokines like tumor necrosis factor. Potential CV comorbidities should be assessed in all patients with PsD through clinical history, risk factor assessment (eg, diabetes, hypertension, dyslipidemia), blood tests, and imaging, when required. Collaboration with the patient's primary care physician is essential, offering preventive measures such as healthy lifestyle advice (eg, diet, exercise, weight loss, smoking and drinking cessation). Management of comorbid conditions requires a multidisciplinary setting of family doctors, internists, cardiologists, dermatologists, and rheumatologists. Treating psoriatic arthritis and psoriasis to remission is recommended; however, the data on CVD risk modification remain inconclusive, necessitating further studies. Thus, routine CVD assessment and management should be provided to patients with PsD, despite practical difficulties such as clinical time constraints and lack of support staff. The evidence and experiences of a dermatologist and rheumatologist assessing and managing CVD in clinic were presented at the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2024 annual meeting.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".