Sinus Tachycardia: a Multidisciplinary Expert Focused Review
Bibliographic record
Abstract
Sinus tachycardia (ST) is ubiquitous, but its presence outside of normal physiological triggers in otherwise healthy individuals remains a commonly encountered phenomenon in medical practice. In many cases, ST can be readily explained by a current medical condition that precipitates an increase in the sinus rate, but ST at rest without physiological triggers may also represent a spectrum of normal. In other cases, ST may not have an easily explainable cause but may represent serious underlying pathology and can be associated with intolerable symptoms. The classification of ST, consideration of possible etiologies, as well as the decisions of when and how to intervene can be difficult. ST can be classified as secondary to a specific, usually treatable, medical condition (eg, pulmonary embolism, anemia, infection, or hyperthyroidism) or be related to several incompletely defined conditions (eg, inappropriate ST, postural tachycardia syndrome, mast cell disorder, or post-COVID syndrome). While cardiologists and cardiac electrophysiologists often evaluate patients with symptoms associated with persistent or paroxysmal ST, an optimal approach remains uncertain. Due to the many possible conditions associated with ST, and an overlap in medical specialists who see these patients, the inclusion of experts in different fields is essential for a more comprehensive understanding. This article is unique in that it was composed by international experts in Neurology, Psychology, Autonomic Medicine, Allergy and Immunology, Exercise Physiology, Pulmonology and Critical Care Medicine, Endocrinology, Cardiology, and Cardiac Electrophysiology in the hope that it will facilitate a more complete understanding and thereby result in the better care of patients with ST.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".