Eat, stress, inflame: examining the link between chronic stress, coronary inflammation and plaque
Bibliographic record
Abstract
This editorial refers to ‘Association between inflammatory biomarkers, chronic stress, and pericoronary adipose tissue attenuation obtained with coronary CT’, by T. Albertini et al., https://doi.org/10.1093/ehjci/jeaf217. Stress is ubiquitous in modern life, triggered by a milieu of sources from the workplace to finance and home life. In our early evolution, the stress response was a critical physiological reaction to external risks, eliciting a cascade of changes within the body that best positioned it to respond to these threats. In modernity, chronic activation of the stress response has turned this once physiological survival mechanism into a pathological state that increases risk of cardiovascular disease on par with more traditional risk factors.1 Chronic stress results in increased amygdala activity, bone marrow activation and vascular inflammation.2 In addition, elevated stress-related inflammatory cytokines are associated with atherosclerosis and increased risk of myocardial infarction (MI).3 While the relationship of stress with systemic inflammatory markers and large vessel inflammation has been well documented, the interaction of this with coronary inflammation is less well explored.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".