Bibliographic record
Abstract
A plethora of risk factors, such as hypercholesterolemia, smoking, hypertension, and others lead to the progression of coronary atherosclerosis. Vulnerable plaques are formed, and rupture of such plaques results in the development of myocardial infarction. Great progress has been made in the medical community's focus on management of risk factors, with clear improvement in the incidence and outcome of myocardial infarction. However, triggers of plaque rupture, which include significant physical and mental stress, need more attention. In this report, we focused on the effect of emotional stress in triggering various acute cardiac events. Natural disasters such as earthquakes result in significant emotional stress, and have been associated with substantial increases in cardiac death and acute myocardial infarction. This is more pronounced with severe events, particularly if they occur in the early morning hours. Anger and severe emotional stress from various life events, particularly from stressed marital relations or stressful working conditions, will result in markedly increased occurrence of myocardial infarction. This is more pronounced in patients with known coronary artery disease or significant risk factors. Providers need to focus on management of stress during hospitalization for myocardial infarction, as well as in the rehabilitation phase of such events.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".