A Systematic Review of Noninvasive Electrocardiogram Monitoring Devices for the Evaluation of Suspected Cardiovascular Syncope
Bibliographic record
Abstract
The aims of this study were (1) to identify research publications studying noninvasive electrocardiogram (ECG) monitoring devices, (2) to define and categorize current technology in noninvasive ECG recording, and (3) to discuss desirable noninvasive recording features for personalized syncope evaluation to guide technological advancement and future studies. We performed a systematic review of the literature that assessed noninvasive ECG-monitoring devices, regardless of the reason for monitoring. We performed an Internet search and corresponded with syncope experts and companies to help identify further eligible products. We extracted information about included studies and device features. We found 173 relevant papers. The main reasons for ECG monitoring were atrial fibrillation (n = 45), coronary artery disease (n = 10), syncope (n = 8), palpitations (n = 8), other cardiac diseases (n = 67), and technological aspects of monitoring (n = 35). We identified 198 devices: 5 hospital telemetry devices, 12 patches, 46 event recorders, 70 Holter monitors, 23 external loop recorders, 20 mobile cardiac outpatient telemetries, and 22 multifunctional devices. The features of each device were very heterogeneous. There are a large number of ECG-monitoring devices with different features available in the market. Our findings may help clinicians select the appropriate device for their patients. Since there are only a few published articles analyzing their usefulness in syncope patients, further research might improve their use in this clinical setting.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".