The Utility of Holter Monitoring Compared to Loop Recorders in the Evaluation of Syncope and Presyncope
Bibliographic record
Abstract
Background:Holter monitoring is frequently used to assess patients with syncope, but rarely provides a diagnosis. Newer loop recorders provide the opportunity for prolonged electrocardiographic monitoring to enhance diagnostic yield. Methods:The results of 232 Holter monitors and 81 loop recordings performed for the investigation of syncope or presyncope were reviewed for indication, patient demographics, and presence and type of symptoms and/or arrhythmias. The results were classified as (1) symptom‐arrhythmia correlation, (2) clinically useful information (group 1 plus those excluding arrhythmic syncope, and those demonstrating asymptomatic serious arrhythmias) and (3) unhelpful (asymptomatic and no serious arrhythmias). Results:Loop recorders provided a symptom‐arrhythmia correlation in 11.1% of patients compared to only 0.4% in the Holter group (P < 0.0001). Clinically useful information was obtained in 54.3% of loop patients compared to 27.6% in the Holter group (P < 0.0001). Technical problems occurred in 0.4% of the Holter patients and in 3.7% of loop patients (P = 0.05). Classification was difficult in seven patients in the Holter group; two experienced symptoms during sinus rhythm but also had a serious asymptomatic arrhythmia, and five patients had 6–10 beats of asymptomatic ventricular tachycardia at a rate < 160 beats/min. Conclusion:Loop recording was well tolerated and superior to Holter monitoring in providing a symptom‐arrhythmia correlation or clinically useful information in patients with syncope and presyncope. An initial approach with a loop‐recording device should be employed in these patients.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".