Impact of biological sex on valvular heart disease, interventions, and outcomes
Bibliographic record
Abstract
Valvular heart disease (VHD) is common, affecting >14% of individuals aged >75, and is associated with morbidity, including heart failure and arrhythmia, and risk of early mortality. Increasingly, important sex differences are being found between males and females with VHD. These sex differences can involve the epidemiology, pathophysiology, presentation, diagnosis, and outcomes of the disease. Females are often disadvantaged, and female sex has been shown to be associated with delayed diagnosis and inferior outcomes in various forms of VHD. In addition, the unique pathophysiologic state of pregnancy is associated with increased risk for maternal and fetal morbidity and mortality in many forms of VHD. Therefore, understanding and recognizing these sex differences, and familiarity with the attendant risks of pregnancy and management of pregnant females with VHD, is of great importance for any primary care or cardiovascular medicine practitioner caring for the female patient. This review will outline sex differences in aortic, mitral, pulmonic, and tricuspid VHD, with particular focus on differences in pathophysiology, clinical presentation, and outcomes. In addition, the pathophysiology and management implications of pregnancy will be discussed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".