Importance of sex and gender in ischaemic stroke and carotid atherosclerotic disease
Bibliographic record
Abstract
Stroke is a leading cause of death and disability worldwide. Women are disproportionately affected by stroke, exhibiting higher mortality and disability rates post-stroke than men. Clinical stroke research has historically included mostly men and studies were not properly designed to perform sex- and gender-based analyses, leading to under-appreciation of differences between men and women in stroke presentation, outcomes, and response to treatment. Reasons for these differences are likely multifactorial; some are due to gender-related factors (i.e. decreased social support, lack of stroke awareness), yet others result from biological differences between sexes. Unlike men, women often present with 'atypical' stroke symptoms. Lack of awareness of 'atypical' presentation has led to delays in hospital arrival, diagnosis, and treatment of women. Differences also extend to carotid atherosclerotic disease, a cause of stroke, where plaques isolated from women are undeniably different in morphology/composition compared to men. As a result, women may require different treatment than men, as evidenced by the fact that they derive less benefit from carotid revascularization than men but more benefit from medical management. Despite this, women are less likely than men to receive medical therapy for cardiovascular risk factor management. This review focuses on the importance of sex and gender in ischaemic stroke and carotid atherosclerotic disease, summarizing the current evidence with respect to (i) stroke incidence, mortality, awareness, and outcomes, (ii) carotid plaque prevalence, morphology and composition, and gene connectivity, (iii) the role of sex hormones and sex chromosomes in atherosclerosis and ischaemic stroke risk, and (iv) carotid disease management.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".