South Asians and cardiometabolic health: A framework for comprehensive care for the individual, community, and population - An American society for preventive cardiology clinical practice statement
Bibliographic record
Abstract
South Asians (SAs) represent an increasing proportion of North American populations and demonstrate excess cardiometabolic risk. Multiple factors likely contribute; however, much is not yet known about what leads to this excess risk. Diet composition, physical activity, and mental health are important lifestyle contributors. Specific adverse pregnancy outcomes are higher in SA women and represent an early opportunity for intervention. More broadly, comprehensive assessments of adiposity, diabetes, hypertension, dyslipidemia, coronary atherosclerosis via imaging, and genetic risk may improve detection and awareness among SAs and those treating SAs. At an individual level, culturally tailored preventive clinics may foster awareness and detection, leading to improved prevention and management of cardiometabolic risk. At a community and population level, assessments of the impact of social determinants, acculturation, and the environment may lead to broader initiatives to improve health in SAs. Lastly, supporting expanded investigation, policy, and other health and science measures at an institutional and societal level may lead to broad but impactful changes across the North American diaspora. In this clinical practice statement, we aim to provide a roadmap of the path forward in each of these domains for health care providers and health systems, community outreach groups, and stakeholders invested in investigation and policy to mitigate risk and empower SAs to lead healthy lives.
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 | 0.047 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".