Remote Electrocardiographic Monitoring with a Wireless Implantable Loop Recorder: Minimizing the Data Review Burden
Bibliographic record
Abstract
INTRODUCTION: Currently available implantable loop recorders (ILRs) are hampered by limited memory, sensing artifacts, and need for manual memory download. Remote monitoring techniques that automatically transfer stored recordings for review may enhance ILR utility. However, automatic electrocardiograph (ECG) detection and transmission of an excessive number of tracings directly to physicians may be burdensome. This pilot study assessed the utility of direct ILR transmission to a central ECG monitoring center on the burden of data to be reviewed by the physician. METHODS: Patients with unexplained syncope were implanted with a novel ILR with automatic (i.e., independent of patient intervention) wireless telemetry download. Transmitted recordings underwent a two-step review process: initial algorithmic filtering followed by human overread at a monitoring center using predefined criteria. RESULTS: Forty patients were enrolled and followed for 8.5 ± 5.1 months. A total of 223,226 ECG recordings were transmitted to the monitoring center (on average 660 per patient per month). Algorithmic filtering eliminated 191,305 ECGs as artifact (89%), with monitoring center overread of 31,921 strips. Ultimately, 117 relevant ECGs were selected for further evaluation by the physician (0.0053%). One or more relevant ECGs were identified for 20 patients (50%). CONCLUSIONS: Automatic ILR recording and wireless technique is feasible for remote ECG monitoring by ILRs. However, sensitive criteria for recording and transmission may result in an excessive ECG burden. The two-step screening process in this pilot study minimized physician overread time while providing clinically relevant recordings in a substantial proportion of 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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".