Heart rate: control mechanisms, pathophysiology and assessment of the neurocardiac system in health and disease
Bibliographic record
Abstract
The monitoring of physiological function and dysfunction is an important principle in modern medicine. Heart rate is a basic example of this type of observation, particularly assessing the neurocardiac system, which entails the autonomic nervous system and intracardiac processes. The neurocardiac axis is an underappreciated and often overlooked system which, if measured appropriately in the clinical setting, may allow identification of patients at risk of disease progression and even mortality. While heart rate itself is a simplistic tool, more information may be gathered through assessing heart rate variability and heart rate recovery time. Studies have demonstrated an association of slow heart rate recovery and lower heart rate variability as markers of elevated sympathetic and lower parasympathetic tone. These parameters have additionally been shown to relate to development of arrhythmia, heart failure, systemic inflammatory processes, ischaemic heart disease and an increased rate of mortality. The aim of this review is to detail how heart rate is homeostatically controlled by the autonomic nervous system, how heart rate can impact on pathophysiological processes, and how heart rate variability and heart rate recovery time may be used in the clinical setting to allow the neurocardiac system to be assessed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".