A review of the common causes of acute coronary syndrome (ACS) in females, and the role of estrogen on its pathologies and risk factors—review article
Bibliographic record
Abstract
As our understanding of acute coronary syndromes (ACS) has increased, we have been able to better delineate the unique pathologies that cause ACS. This review article on the common causes of ACS in females explores the atherosclerotic pathologies of plaque rupture and plaque erosion, and non-atherosclerotic pathologies of SCAD, MINOCA, and Takotsubo cardiomyopathy. It reviews the literature on the link between estrogen and its protection against both atherosclerotic risk factors and induction of plaque vulnerability. This review also explores the mechanistic plausibility that estrogen may contribute to the increased risk of SCAD, MINOCA, and Takotsubo cardiomyopathy-although these mechanisms remain under investigation. Whilst there is still much to be researched about these pathologies, an analysis of the biological impact of sex hormones at the molecular level has helped identify links between mechanisms suggesting a possible unifying pathology between plaque erosion, MINOCA, and microvascular spasm. In this way, a new paradigm for ACS as an equilibrium between thrombus-stabilisation and thrombus-dissolution states is also explored in this article. What has become evident is that the attempt to understand the common causes of ACS in females has resulted also in a deeper understanding of atherosclerotic ACS in males, and highlighted areas of exciting further discovery.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".