Managing patients with heart failure: contemporary real-world experience
Bibliographic record
Abstract
OBJECTIVE: Heart failure (HF) is a chronic disease with growing numbers of patients and a significant compromise in quality of life and high mortality. The main purpose of this study was to evaluate the current practices in managing patients with HF among patients admitted to the hospital and discharged with a primary diagnosis of HF and patients managed in the heart function clinic. RESULTS: This study is a retrospective chart review of patients admitted to the hospital and discharged with a primary diagnosis of HF. A total of 448 patient charts were reviewed, of which 173 patients were in the hospital group and 275 patients in the Clinic group. 278 (62.1%) were men, and 170 (37.9%) were women. The Clinic group of patients were significantly received guideline-directed medical therapy (Beta-blockers, Angiotensin-converting enzyme inhibitors, Angiotensin receptor blockers, Diuretics, Mineralocorticoid receptor antagonists-p < 0.001). The Clinic group of patients (17.1%) were significantly less re-hospitalized (p < 0.001) compared to the Hospital group (28%) at 180 days. Physician led multidisciplinary Heart function clinics have better adherence to guideline directed medical therapy and significantly lower rates of re-hospitalization thereby providing cost effective heart failure management with usual care.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".