Heart failure disease management program, its contribution to established pharmacotherapy and long-term prognosis in real clinical practice - retrospective data analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND AIMS: The prognosis of patients with heart failure (HF) is still generally unfavorable. HF with reduced ejection fraction (HFrEF) patients reach target medication doses in very low percentages in daily clinical practice. HF disease management programs (DMP), including nurse and telemedicine support that facilitate achieving target medication doses, may improve the unfavorable prognosis. METHODS: We retrospectively analyzed the data of 738 patients with HFrEF who were followed in a single HF center during the years 1975-2011, for 6.4 (median) years. DMP, nurse and telemedicine support is established at this center. RESULTS: The group achieved left ventricle (LV) recovery after the HF treatment. The median LV ejection fraction improved from 25.0% at baseline to 50.0% at the time of the latest data collection. The proportion of NYHA II, III and IV classes decreased from 27.6%, 30.2% and 29.7% to 26.6%, 7.2% and 0.1%, respectively while the proportion of NYHA class I increased from 12.5% to 66.1%. Median NT-proBNP decreased from 975.0 to 324.0 pg/mL. The survival of the patient group was favorable; 79.7% survived 18.1 years after diagnosis of HF. A high percentage of the patients received recommended target or higher than target doses of angiotensin-converting enzyme inhibitors (82.0%) and beta-blockers (78.1%). CONCLUSION: The established pharmacotherapy resulted from an effective DMP and this contributed to the favorable prognosis.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it