Patch monitors for arrhythmia monitoring in patients for suspected inherited arrhythmia syndrome
Bibliographic record
Abstract
INTRODUCTION: Patients undergoing evaluation for an inherited arrhythmia syndrome undertake a series of ambulatory investigations including 24-h Holter monitor, exercise treadmill testing (ETT), and others. Patch monitors may simplify the evaluation, providing accurate arrhythmia evaluation and QT assessment. METHODS AND RESULTS: Patients referred for evaluation of an inherited arrhythmia syndrome underwent standard investigations, including 12-lead electrocardiography (ECG), 24-h Holter monitoring, ETT, along with supplemental monitoring using a 7-day ECG patch monitor. Heart rates (HR), corrected QT intervals (QTc), and ectopic burden were compared across monitoring modalities. Among 35 patients that wore the patch monitor, the median age was 39 years (54% male). There was intermediate correlation between resting HR across modalities (r = .58-.66) and poor correlation of peak HR (r = .27-.39). There was intermediate correlation between resting QTc intervals across modalities (r = .72-.77) but negligible correlation between QTc intervals at peak HR across modalities (r = -.01 to -.06). There was good correlation in PAC and PVC ectopic burden across the Holter and patch monitor. CONCLUSION: Patch monitors may simplify the evaluation of patients for an inherited arrhythmia syndrome and provide resting QT assessment over time. However, QTc interval comparison at peak HRs remains variable, and may be limited by the single-lead ECG vector when using the patch monitor. Apart from QTc intervals at peak HR, patch monitors demonstrated good correlation with the ECG and Holter monitor for other parameters.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".