Environmental stressors and cardio-metabolic disease: part II–mechanistic insights
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
Environmental factors can act as facilitators of chronic non-communicable diseases. Ambient noise and air pollution collectively outrank all other environmental risk factors in importance, contributing to over 75% of the disease and disability burden associated with known environmental risk factors. In the first part of this review, we discussed the global burden and epidemiologic evidence supporting the importance of these novel risk factors as facilitators of cardiometabolic disease. In this part, we will discuss pathophysiological mechanisms responsible for noise and air pollution-mediated effects. Akin to traditional cardiovascular risk factors, a considerable body of evidence suggests that these environmental agents induce low-grade inflammation, oxidative stress, vascular dysfunction, and autonomic nervous system imbalance, thereby facilitating the development of diseases such as hypertension and diabetes. Through their impact on traditional risk factors and via additional novel mechanisms, environmental risk factors may have much larger impact on cardiovascular events than currently appreciated. In the second part of this review, we discuss deficiencies and gaps in knowledge and opportunities for new research.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it