A Review of Cardiovascular Risk Factors in Women with Psychosis
Bibliographic record
Abstract
The presence of medical comorbidities in women with psychotic disorders can lead to poor medical and psychiatric outcomes. Of all comorbidities, cardiovascular disease is the most frequent, and the one most likely to cause early death. We set out to review the evidence for cardiovascular risk factors (CRFs) in women with schizophrenia-related disorders and for interventions commonly used to reduce CRFs. Electronic searches were conducted on PubMed and Scopus databases (2017–2022) to identify papers relevant to our aims. A total of 17 studies fulfilled our inclusion criteria. We found that CRFs were prevalent in psychotic disorders, the majority attributable to patient lifestyle behaviors. We found some inconsistencies across studies with regard to gender differences in metabolic disturbances in first episode psychosis, but general agreement that CRFs increase at the time of menopause in women with psychotic disorders. Primary care services emerge as the best settings in which to detect CRFs and plan successive intervention strategies as women age. Negative symptoms (apathy, avolition, social withdrawal) need to be targeted and smoking cessation, a heart-healthy diet, physical activity, and regular sleep routines need to be actively promoted. The goal of healthier hearts for women with psychotic disorders may be difficult, but it is achievable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".