Toward a Cardio-Environmental Risk Model: Environmental Determinants of Cardiovascular Disease
Bibliographic record
Abstract
It is increasingly recognized that strong geographic variations in cardiovascular risk cannot be explained using traditional cardiovascular risk factors alone. Indeed, it is highly unlikely that heredity and classic risk factors such as hypertension, diabetes, dyslipidemia, and tobacco use can explain the tenfold variation observed in cardiovascular mortality among men in Russia and those in Switzerland. Since the advent of industrialization and resultant changes to our climate, it is now clear that environmental stressors also influence cardiovascular health and our thinking around cardiovascular risk prediction is in need of a paradigm shift. Herein, we review the basis for this shift in our understanding of the interplay of environmental factors with cardiovascular health. We illustrate how air pollution, hyperprocessed foods, the amount of green space, and population activity levels are now considered the 4 major environmental determinants of cardiovascular health and provide a framework for how these considerations might be incorporated into clinical risk assessment. We also outline the clinical and socioeconomic effects of the environment on cardiovascular health and review key recommendations from major medical societies.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".