Management of heart failure patients with systolic dysfunction in a real world setting-a physician-based survey
Bibliographic record
Abstract
Background: The objective of this study was to understand cardiologists’ perspectives on heart failure (HF) management with an emphasis on heart rate (HR) optimization and practice patterns among different medical specialties. Methods: A digital, cross-sectional, questionnaire-based survey involving 149 Indian cardiologists who were experienced in the management of patients with HF in their clinical practice was conducted. The survey questionnaire included 53 items divided into five sections. Responses were analyzed and data were represented as summary statistics. Results: According to most cardiologists, majority of patients belong to the New York Heart Association (NYHA) categories II and III, with ischemia being the most prevalent cause of HF. For patients with HF with reduced ejection fraction (HFrEF), HR>70 beats per minute and sinus rhythm, 38.9% of clinicians strongly agreed to include ivabradine in the treatment regimen. According to 56.4% of clinicians, 26%-50% of patients with HFrEF were receiving ivabradine therapy at <50% guideline-directed target dose of β-blockers. At the highest therapeutic dosage of ivabradine, 46.3% of clinicians noticed a 6-10 bpm reduction in HR. Additionally, it was reported that a stable HFrEF patient consumed an average of 4-6 tablets daily (67.1%), which increased the pill burden. Overall, 58.4% and 67.1% of clinicians strongly believed that cutting back on medications will assist with therapy adherence and that improved therapy adherence and compliance aid with clinical outcomes, respectively. Majority of the clinicians strongly agreed or agreed that patients should be switched from twice-daily to once-daily ivabradine. Conclusions: Clinical outcomes of patients with HF could be improved by reducing the pill burden and improving compliance.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".