The Relationship between 10 Years Risk of Cardiovascular Disease and Schizophrenia Symptoms: Preliminary Results
Bibliographic record
Abstract
OBJECTIVE: Previous research shows that patients with schizophrenia have increased cardiovascular disease risk than general population. Increased cardiovascular risk in schizophrenia patients have been associated with many reasons such as antipsychotic drugs, genetic predisposition, andlifestyle. In this study, we aimed to investigate the relationship between the risk of heart disease and schizophrenia symptomatology. METHODS: The 10-year cardiovascular risk was assessed by the Framingham Risk Score (FRS) in 103 patients with schizophrenia and in 39 healthy controls. Sociodemographic characteristics, age at schizophrenia onset, duration of illness, number of hospitalizations, the course of the disease and antipsychotic medications were recorded. Patients' symptoms were evaluated via The Scale for the Assessment of Negative Symptoms (SANS), The Scale for the Assessment of Positive Symptoms (SAPS), and Calgary Depression Scale for Schizophrenia (CDSS). RESULTS: Ten-year cardiovascular risk was 5.16% inpatients with schizophrenia, and 3.02% in control group (p=0.030). No significant correlation was found between FRS scores, SANS, SAPS, and CDSS scores. However, FRS scores were significantly correlated with age, number of hospitalizations and duration of disease (r=0.300, 0.261, 0.252, respectively). Moreover FRS scores were higher (p=0.008) and high-density lipoprotein (HDL) levels were lower (p=0.048) in patients using multiple antipsychotics. CONCLUSION: Our findings suggest a relationship between the risk of cardiovascular disease and the duration and overall severity of schizophrenia and also highlights the role of antipsychotics in this relationship.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".