Gender and Other Disparities in Referral to Specialized Heart Failure Clinics Following Emergency Department Visits
Bibliographic record
Abstract
BACKGROUND: Persons with heart failure (HF) at high risk for adverse events should be followed by specialized HF clinics, since follow-up by specialized HF clinics improves outcomes for HF patients. The objective was to determine whether there were disparities for gender and other factors associated with referral of patients to specialized HF clinics. METHODS: In this prospective cohort study, patients with a confirmed primary diagnosis of HF were recruited by nurses at 8 hospital emergency departments (ED) in Québec, Canada. They were interviewed by telephone at 6 weeks post ED discharge and subsequently at 3 months and 6 months. Pertinent clinical variables were extracted from medical charts by trained nurses. Bivariate analysis and multiple logistic regression were used to identify whether gender and other potential factors were associated with referral to the HF clinic. RESULTS: We enrolled 549 patients (mean age 75.5±11.0 years; 51% males). By 6 months after their ED visit for HF, 37.6% of the cohort were referred to specialized HF clinics. Men were more likely to be referred (odds ratio [OR] 2.04; 95% confidence interval [CI] 1.12, 3.74). Other factors associated with referral were younger age (OR 0.95; 95% CI 0.92, 0.98), and systolic dysfunction HF (left ventricle ejection fraction <40%) (OR 3.08; 95% CI 1.77, 5.46). CONCLUSION: There are disparities in referral with respect to gender, age, and type of HF. These disparities in referral need to be addressed.
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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 | 0.004 |
| 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) | 0.003 | 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".