Review: Rapid assessment of patients with palpitations
Bibliographic record
Abstract
Background.Palpitations are one of the most prevalent general practice presentations and a concerning aetiology for cardiac causes.Since many physiological and pathological causes induce palpitations, doctors tend to conduct long-term, expensive evaluations, laboratory tests and specialised examinations.In addition to increasing medical expenses, more evaluations can lead to distress for patients and their families.While palpitations tend to be benign, on the other hand, they may sometimes have lifethreatening implications.Objectives.To summarise the current study on rapid assessment of patients with palpitations; therefore, patients will receive adequate management and treatment effectively. Material and methods.A comprehensive electronic search was conducted using PubMed, Google Scholar, SAGE and ScienceDirect.The following search keywords were used: palpitation, assessments, ECG, emergency and diagnostic testing.The search was limited to English-language publications from 1990 to 2021.Manual searching of relevant journals and reference lists was also performed.Results.Palpitations are a common clinical sign caused by various factors.Palpitations are a frequent clinical symptom with a wide range of causes.A systematic rapid assessment can initially identify palpitations.Conclusions.If the patient is examined during palpitations, the earliest echocardiogram (ECG) record is important in the diagnostic approach while waiting for further workup.A detailed history taking is then necessary to narrow the cause of palpitations.A comprehensive history helps determine which testing and monitoring will be needed.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".