Gender Differences in Utilization of Specialized Heart Failure Clinics
Bibliographic record
Abstract
BACKGROUND: Although heart failure (HF) prevalence is equally high among men and women, observed differences in the provision of care are still not fully understood. We sought to evaluate gender differences in patient profiles, diagnostic testing, medication prescription, and referrals in specialized multidisciplinary ambulatory HF clinics in Ontario. MATERIALS AND METHODS: Medical chart abstraction was conducted first by randomly selecting 9 (out of 34) HF clinics in Ontario, and then by randomly selecting 100 patient records in each clinic. Data on patient demographics, comorbidities, diagnostic tests, medication use, and referrals were abstracted, covering a period from the first clinic visit up to 1 year. Descriptive statistics and regression analysis were used to assess gender differences. RESULTS: Of the 884 patients, only 314 were women (35.5%). At the first clinic visit, women were older, had better systolic function but worse functional status, and had a lower prevalence of hyperlipidemia, diabetes, and smoking than men. There were more women with non-ischemic HF etiology than men (63.9% vs. 43.3%, p < 0.001). Adjusted analysis did not reveal gender differences in the average number of echocardiographic assessments and in the prescription rates of evidence-based medications. Men were twice more likely to be referred to electrophysiology studies than women (18.6% vs. 7.8%, p < 0.001). The rates of dietary counseling and cardiac rehabilitation referrals were similarly low in both groups. CONCLUSIONS: More men than women are treated in specialized ambulatory HF clinics. Although women differ from men in selected clinical characteristics, no major differences were observed in patient management. The reasons for low enrollment rates of women into the HF ambulatory clinics need further investigation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".