Cardiovascular risk and bipolar disorder: factors associated with a positive coronary calcium score in patients with bipolar disorder type 1
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular disease is the leading cause of death in patients with bipolar disorder. The aim of this study was to evaluate the factors associated with positive coronary calcium score (CCS) in individuals with bipolar disorder type 1. METHODS: Patients from the Bipolar Disorder Program at Hospital de Clínicas de Porto Alegre, Brazil, underwent computed tomography scanning for calcium score measurement. Clinical and sociodemographic variables were compared between patients according to their CCS status: negative (CCS = 0) or positive (CCS > 0). Poisson regression analysis was used to examine the association of CCS with number of psychiatric hospitalizations. RESULTS: Out of 41 patients evaluated, only 10 had a positive CCS. Individuals in the CCS-positive group were older (55.2±4.2 vs. 43.1±10.0 years; p = 0.001) and had more psychiatric hospitalizations (4.7±3.0 vs. 2.6±2.5; p = 0.04) when compared with CCS- negative subjects. The number of previous psychiatric hospitalizations correlated positively with CCS (p < 0.001). CONCLUSION: Age and number of psychiatric hospitalizations were significantly associated with higher CCS, which might be a potential method for diagnosis and stratification of cardiovascular disease in bipolar patients. There is a need for increased awareness of risk assessment in this population.
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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".