Does This Patient With Palpitations Have a Cardiac Arrhythmia?
Bibliographic record
Abstract
CONTEXT: Many patients have palpitations and seek advice from general practitioners. Differentiating benign causes from those resulting from clinically significant cardiac arrhythmia can be challenging and the clinical examination may aid in this process. OBJECTIVE: To systematically review the accuracy of historical features, physical examination, and cardiac testing for the diagnosis of cardiac arrhythmia in patients with palpitations. Data Source, Study Selection, and DATA EXTRACTION: MEDLINE (1950 to August 25, 2009) and EMBASE (1947 to August 2009) searches of English-language articles that compared clinical features and diagnostic tests in patients with palpitations with a reference standard for cardiac arrhythmia. Of the 277 studies identified by the search strategy, 7 studies were used for accuracy analysis and 16 studies for diagnostic yield analysis. Two authors independently reviewed articles for study data and quality and a third author resolved disagreements. DATA SYNTHESIS: Most data were obtained from single studies with small sample sizes. A known history of cardiac disease (likelihood ratio [LR], 2.03; 95% confidence interval [CI], 1.33-3.11), having palpitations affected by sleeping (LR, 2.29; 95% CI, 1.33-3.94), or while the patient is at work (LR, 2.17; 95% CI, 1.19-3.96) slightly increase the likelihood of a cardiac arrhythmia. A known history of panic disorder (LR, 0.26; 95% CI, 0.07-1.01) or having palpitations lasting less than 5 minutes (LR, 0.38; 95% CI, 0.22-0.63) makes the diagnosis of cardiac arrhythmia slightly less likely. The presence of a regular rapid-pounding sensation in the neck (LR, 177; 95% CI, 25-1251) or visible neck pulsations (LR, 2.68; 95% CI, 1.25-5.78) in association with palpitations increases the likelihood of a specific type of arrhythmia (atrioventricular nodal reentry tachycardia). The absence of a regular rapid-pounding sensation in the neck makes detecting the same arrhythmia less likely (LR, 0.07; 95% CI, 0.03-0.19). No other features significantly alter the probability of clinically significant arrhythmia. Diagnostic tests for prolonged periods of electrocardiographic monitoring vary in their yield depending on the modality used, duration of monitoring, and occurrence of typical symptoms during monitoring. Loop monitors have the highest diagnostic yield (34%-84%) for identifying an arrhythmia. CONCLUSIONS: While the presence of a regular rapid-pounding sensation in the neck or visible neck pulsations associated with palpitations makes the diagnosis of atrioventricular nodal reentry tachycardia likely, the reviewed studies suggest that the clinical examination is not sufficiently accurate to exclude clinically significant arrhythmias in most patients. Thus, prolonged electrocardiographic monitoring with demonstration of symptom-rhythm correlation is required to make the diagnosis of a cardiac arrhythmia for most patients with recurrent palpitations.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| 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".