Gender differences in the etiology of heart failure: A systematic review
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is an increasing problem for the aging population, specifically among women. The etiology of HF influences both the selection and outcome of the treatment. There are variations between genders in morbidity and mortality in different studies, possibly reflecting etiology. The objective of this study was to examine the strength of evidence available for gender differences in the etiology of chronic heart failure. METHODS: Computer-assisted searches from 1980-2009 for gender differences in the etiology of heart failure were performed (Medline, EMBASE and PubMed). From 2347 abstracts reviewed based on inclusion criteria, 35 original articles were chosen for review. Data extraction was based on observational studies (prospective/retrospective cohort or cross sectional) with a mean follow up of 3 months. There was no interrater variability between the 2 reviewers on data-extraction. RESULTS: Ventricular systolic dysfunction being more associated with male sex, but female sex was more reported to be associated with preserved left ventricular function. Ischemic etiology and associated coronary heart disease were strongly correlated with male sex. The risk for HF was dramatically more elevated for women with systolic hypertension but the association for diabetes mellitus as the etiology of HF was somewhat equal between males and females. CONCLUSIONS: One of the limitations in reaching conclusions about gender differences in cardiovascular disease is that many major clinical trials do not include a gender analysis nor they are powered to do so as women are under-represented in most of the HF studies. The need remains for a well designed prospective study of sufficient numbers of male and female patients with and without heart failure and analyzing etiology and risk factors based on the sex differences.
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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".