Recent Patient Characteristics and Medications at Admission and Discharge in Hospitalized Patients With Heart Failure
Bibliographic record
Abstract
BACKGROUND: To improve the clinical outcome of heart failure (HF), it is important to evaluate the etiology and comorbidities of HF. We previously reported the baseline clinical characteristics and medications in hospitalized patients with HF in years 2000 - 2002 (group 2000) and 2007 - 2009 (group 2008). METHODS: We conducted a retrospective study of 158 patients who were hospitalized due to HF between 2012 and 2014 (group 2013) in the Department of Cardiology, Fukuoka University Hospital. We analyzed the clinical characteristics and medications at admission and discharge, and compared the findings in group 2013 to those in group 2000 and group 2008. RESULTS: The major causes of HF were ischemic heart disease, hypertensive cardiomyopathy, valvular heart disease, and dilated cardiomyopathy. The New York Heart Association classification in group 2013 was significantly higher than those in group 2000 and group 2008. There was no difference in the level of brain natriuretic peptide at admission between group 2008 and group 2013. Tolvaptan began to be administered in group 2013. The median dose of furosemide just before the use of tolvaptan was 40 mg/day. At discharge, group 2013 showed higher rates of β-blocker and aldosterone antagonist. There was no difference in the frequency of loop diuretics. The dose of carvedilol at discharge was only 6.2 ± 4.0 mg/day. Antiarrhythmic drugs and β-blocker were used more frequently in HF with reduced ejection fraction (EF) than in HF with preserved EF. CONCLUSIONS: We may be able to improve the clinical outcome of HF by examining the differences in the clinical characteristics and medications at admission and discharge in hospitalized patients with HF.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".