Implementation of a Cardiopsychiatry Clinic for Cardiovascular Primary Prevention in Individuals with Severe Mental Illness
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Individuals with severe mental illness (SMI), including schizophrenia, schizoaffective disorder, and bipolar disorder, face twice the risk of cardiovascular disease compared with the general population. However, disparities in cardiovascular care access persist. We established a cardiopsychiatry clinic to assess cardiovascular risk and initiate evidence-based treatment in this population. METHODS: From November 2022 to July 2023, patients with SMI but no known cardiovascular disease were referred from outpatient psychiatry clinics to a tertiary care hospital in Canada. Cardiovascular risk factors were recorded, and the 10-year Framingham Risk Score was calculated. Treatment was initiated based on clinical guidelines. RESULTS: Among 23 participants (mean age 52.6 ± 10.8 years; 30% female), schizophrenia was the most common SMI (65%). The mean Framingham Risk Score was 13.8% ± 8.1. Smoking (61%) and obesity (74%) were the most prevalent risk factors. More than half (57%) had newly identified cardiovascular risks, leading to pharmacological treatment initiation for metabolic syndrome/dyslipidemia (N = 7), hypertension (N = 7), and diabetes (N = 2). CONCLUSIONS: The cardiopsychiatry clinic is a pilot project that highlights the need for integrated cardiovascular screening and treatment for individuals with SMI. Larger studies are required to address persistent gaps in cardiovascular care for this high-risk population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it