ICD Monitoring Zones: Intricacies, Pitfalls, and Programming Tips
Bibliographic record
Abstract
Implantable cardioverter-defibrillators (ICD) are widely regarded as the treatment of choice for primary and secondary prevention against sudden cardiac death across a broad spectrum of underlying pathologies. Over the past 20 years, ICDs have evolved into complex multifunctional units capable of recording, chronicling, self-testing, and delivering interventional therapies. Technological advances permitted the creation of ICD monitoring zones that are now considered valuable in diagnosing slower, presumably more stable ventricular arrhythmias. They may be helpful especially in patients with unexplained symptoms such as palpitations and/or syncope, particularly in the setting of antiarrhythmic pharmacological therapy that may slow ventricular tachyarrhythmias. Caregivers largely view ICD monitoring zones as passive features that do not interfere or interact with appropriate functioning of active treatment zones. As will be discussed in this clinical review, this is not always the case. Herein, we unravel the intricacies regarding monitoring zone functions and algorithms, highlight potential pitfalls, and offer practical programming tips relevant to each device manufacturer.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".