An approach towards individualized lower rate settings for pacemakers
Bibliographic record
Abstract
Key Findings▪Without evidence-based guidance, the pacemaker lower rate limit is typically left at 60 beats per minute, which is much lower than the average adult resting heart rate of 71–79 beats per minute based on large cohorts.▪While low heart rates are beneficial for patients with systolic dysfunction, pacing at a more physiologic heart rate may be a therapeutic target for patients with diastolic dysfunction or heart failure with a preserved ejection fraction (HFpEF).▪Using data from the Centers for Disease Control and Prevention growth charts, the National Health and Nutrition Examination Survey, and the Health-eHeart Study, we demonstrate a negative linear relationship between height and resting heart rate both during human growth and among healthy adult individuals.▪We derived a simple linear regression equation that defines the relationship between height and resting heart rate, which could be used in future studies to investigate a personalized pacemaker lower rate in patients with diastolic dysfunction or HFpEF. ▪Without evidence-based guidance, the pacemaker lower rate limit is typically left at 60 beats per minute, which is much lower than the average adult resting heart rate of 71–79 beats per minute based on large cohorts.▪While low heart rates are beneficial for patients with systolic dysfunction, pacing at a more physiologic heart rate may be a therapeutic target for patients with diastolic dysfunction or heart failure with a preserved ejection fraction (HFpEF).▪Using data from the Centers for Disease Control and Prevention growth charts, the National Health and Nutrition Examination Survey, and the Health-eHeart Study, we demonstrate a negative linear relationship between height and resting heart rate both during human growth and among healthy adult individuals.▪We derived a simple linear regression equation that defines the relationship between height and resting heart rate, which could be used in future studies to investigate a personalized pacemaker lower rate in patients with diastolic dysfunction or HFpEF.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".