Management of congestive heart failure: a gender gap may still exist. Observations from a contemporary cohort
Bibliographic record
Abstract
BACKGROUND: Unlike other cardiovascular diseases the incidence and prevalence of congestive heart failure (CHF) continues to increase. While gender differences in coronary artery disease have been well described, to date, there has been a relative paucity of similar data in patients with CHF. We conducted a pilot study to evaluate the profile and management of patients with CHF at a tertiary care centre to determine if a gender difference exists. METHODS: A chart review was performed at a tertiary care centre on consecutive patients admitted with a primary diagnosis of CHF between June 1997 and 1998. Co-morbidity, diagnostic investigations, and management of CHF were recorded. Comparisons between male and female patients were conducted. RESULTS: One hundred and forty five patients were reviewed. There were 80 male (M) and 65 female (F) patients of similar age [71.6 vs. 71.3 (M vs. F), p = NS]. Male patients were more likely to have had a previous myocardial infarction (66% vs. 35%, p < 0.01) and revascularization (41% vs. 20%, p < 0.05), and had worse left ventricular ejection fraction (LVEF) than women, [median LVEF 3 vs. 2 (M vs. F), p < 0.01]. Male patients were more likely to have a non-invasive assessment of left ventricular (LV) function [85% vs. 69%, (M vs. F), p < 0.05]. A logistic regression analysis suggests that amongst those without coronary disease, males were more likely to receive non-invasive testing. There were no differences in the use of prescribed medications, in this cohort. CONCLUSIONS: This pilot study demonstrated that there seem to be important gender differences in the profile and management of patients with CHF. Importantly women were less likely to have an evaluation of LV function. As assessment of LV function has significant implications on patient management, this data justifies the need for larger studies to assess gender differences in CHF profile and treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".