Cross-disciplinary cardiovascular and psychiatric recommendations: A systematic review of clinical guidelines
Bibliographic record
Abstract
IntroductionIndividuals with serious mental illness (SMI), including major depression, schizophrenia, and bipolar disorder, experience disproportionately high rates of cardiovascular (CV) risk and disease. Despite this well-established connection, it remains unclear how professional society guidelines across cardiology and psychiatry address this relationship.MethodsMajor American and European CV and psychiatric society guidelines published from 2013-2023 were reviewed. Included were guidelines on primary and secondary CV disease prevention, and disease-specific guidelines for schizophrenia, bipolar disorder, and major depressive disorder. Relevant text was extracted and classified as recommendations or supporting text.ResultsTwenty-six guidelines were included (13 CV; 13 psychiatric). Psychiatric considerations appeared in 5 CV guidelines (38%), most commonly addressing mental illness treatment to improve CV outcomes (n = 5), pharmacological considerations (n = 2), and recognition of mental illness as a CV risk factor (n = 2). Only 13% of American CV guidelines included psychiatric content, compared to 80% of European CV guidelines. In contrast, 10 psychiatric guidelines (77%) included CV-related recommendations, including CV screening (n = 16), pharmacological considerations (n = 8), and risk factor control (n = 7). Among psychiatric guidelines, 40% of U.S. and 100% of European documents included CV content.ConclusionsCV considerations are more frequently addressed in psychiatric than psychiatric considerations in CV guidelines. European guidelines showed greater cross-disciplinary integration. These findings highlight the need for more unified, interdisciplinary guidance to reduce CV risk in individuals with SMI.
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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 | 0.021 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".