Findings of remote monitoring of implantable cardioverter defibrillators during the COVID‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: Monitoring of cardiac implantable electronic devices was highly impacted by the COVID-19 pandemic considering the high volume of in-person visits for regular follow-up. Recent recommendations highlight the important role of remote monitoring to prevent exposure to the virus. This study compared remote monitoring of implantable cardioverter defibrillators (ICDs) in patients whose in-person annual visit was substituted for a remote monitoring session with patients who were already scheduled for a remote monitoring session. METHODS: This was a cross-sectional observational study of 329 consecutive patients between 20 March and 24 April 2020. Group 1 included 131 patients whose in-person annual visit was substituted for a remote monitoring session. Group 2 included 198 patients who underwent a remote monitoring session as scheduled in their usual device follow-up. The time interval since the last in-person visit was 13.3 ± 3.2 months in group 1 and 5.9 ± 1.7 months in group 2 (P < .01). RESULTS: In group 1, 15 patients (11.5%) experienced a clinical event compared to 15 patients (7.6%) in group 2 (P = .25). Nineteen patients (14.5%) required a physician intervention in group 1 compared to 19 patients (9.6%) in group 2 (P = .22). Two patients (1.5%) in group 1 and four patients (2.0%) in group 2 required an early in-person follow-up visit during the pandemic (P > .99). CONCLUSION: Remote monitoring of ICDs is useful to identify clinical events and allows physicians to treat patients appropriately during the COVID-19 pandemic regardless of the time interval since their last in-person visit. It reduces significantly in-person visit for regular follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".