Does heart rate really matter to patients with heart failure?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Measurement of heart rate (HR) and rhythm is used to identify patients at increased risk of disease progression, guide selection of treatments and gauge response to therapy. RECENT FINDINGS: Lowering HR with a pure HR lowering agent (ivabradine) in heart failure with reduced ejection fraction (HFrEF) and sinus rate more than 70 beats/min despite beta blockade has been shown to improve outcomes. Additionally, coadministration of ivabradine and beta blockade may enhance symptoms and HR control. In the case of patients with heart failure and preserved ejection fraction (HFpEF), or with paced rhythm, optimal HR control is not known. Also, in atrial fibrillation the relationship between HR and outcomes is not clear and minimal evidence for HR reduction to less than 100 beats/min exists. Reasons for this disconnect between atrial fibrillation and sinus rhythm are not known. SUMMARY: HR continues to be a critical vital sign in assessment and forms the basis for a treatment target in patients with HFrEF at rates more than 70 beats/min. The target for HR patients with HFpEF and those who are paced continuously or in atrial fibrillation is less clear and at present is recommended to be in the 60-100 beats/min range at rest. Further study is needed to refine treatment strategies in these latter patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".