Exposome in ischaemic heart disease: beyond traditional risk factors
Bibliographic record
Abstract
Ischaemic heart disease represents the leading cause of morbidity and mortality, typically induced by the detrimental effects of risk factors on the cardiovascular system. Although preventive interventions tackling conventional risk factors have helped to reduce the incidence of ischaemic heart disease, it remains a major cause of death worldwide. Thus, attention is now shifting to non-traditional risk factors in the built, natural, and social environments that collectively contribute substantially to the disease burden and perpetuate residual risk. Of importance, these complex factors interact non-linearly and in unpredictable ways to often enhance the detrimental effects attributable to a single or collection of these factors. For this reason, a new paradigm called the 'exposome' has recently been introduced by epidemiologists in order to define the totality of exposure to these new risk factors. The purpose of this review is to outline how these emerging risk factors may interact and contribute to the occurrence of ischaemic heart disease, with a particular attention on the impact of long-term exposure to different environmental pollutants, socioeconomic and psychological factors, along with infectious diseases such as influenza and COVID-19. Moreover, potential mitigation strategies for both individuals and communities will be discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".