Bibliographic record
Abstract
OBJECTIVE: To review the physiology of the regulation and determinants of heart rate and the significance in the management of critically ill patients. DATA SOURCES: The MEDLINE database, references from selected articles, and the author's personal database. DATA SYNTHESIS: This review begins with the regulation of cardiac output and heart rate during exercise because this demonstrates the range of physiological responses in the normal human. This analysis shows that change in heart rate is a major component of the cardiovascular system's ability to adjust cardiac output and a number of regulatory systems control heart rate. When heart rate responses are limited because of disease or pharmacologic reasons, changes in stroke volume must compensate, but the capacity to do so is limited by the passive filling characteristics of the ventricles. On the other side, high heart rates increase myocardial oxygen demand, which can be a problem in patients with fixed coronary artery disease. CONCLUSION: Heart rate must be interpreted in the context of the patient's overall hemodynamic condition. The prudent physician must ask why is the heart rate high, what will be achieved by lowering the heart rate, and, finally, what are the consequences of lowering the heart rate?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".