Mental health disorders and their impact on cardiovascular health disparities
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
Mental health disorders are highly prevalent and are associated with significant morbidity, disability, and reduced life expectancy. A key contributor to this disparity is the increased risk of cardiovascular disease (CVD), which is partially driven by inequalities in social determinants of health, healthcare access, and quality of care. To address this challenge, The Lancet Regional Health-Europe convened experts to evaluate the current state of knowledge on inequalities and disparities in cardiovascular health among people with mental health disorders and propose recommendations to address these disparities. This Series paper aims to raise awareness of the disparities in CVD and health-care quality faced by individuals with common mental health conditions such as major depression, anxiety disorders, schizophrenia, bipolar disorder, and posttraumatic stress disorder. There is an urgent need for increased investment, intervention, and research to address the burden of CVD in these populations. Effective management of the comorbidity between mental health disorders and CVD requires an integrated and holistic approach to clinical care that addresses shared risk factors and the complex interactions between physical and mental health.
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.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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