Cloud Follow-Up in Patients With Cardiovascular Implantable Electronic Devices: A Single-Region Study in China
Bibliographic record
Abstract
Background: Due to seriously imbalanced distribution of follow-up clinics in China, routine in-office visits are erratically attended by many cardiovascular implantable electronic device (CIED) patients. Meanwhile, remote monitoring is significantly underutilized. Novel tools to address the current predicament of routine in-office visits in China is urgently needed. Objectives: To assess the reliability and feasibility of cloud follow-up in CIED patients. Methods: A total of 325 CIED patients from 13 hospitals in Sichuan Province, China, were enrolled. Information on patients' sociodemographic and basic clinical characteristics was collected. All devices were tested and programmed with 5G-cloud follow-up platform in a real-time manner. All patients were surveyed about their acceptance of and preferences regarding cloud follow-up compared to routine in-office visits. Results: Compliance with routine in-office visits in this region was 60.6%. None of the patients were enrolled in remote monitoring services. Clinically important predictors of non-compliance were elderly age (≥75 years old), odds ratio (OR) 2.392 (95% confidence interval, 1.111-5.150); needing notification from a follow-up clinic, OR 2.518 (1.179-5.376); and being beyond 15 months post-implantation, OR 5.440 (2.563-11.543). All cloud follow-up sessions were performed safely and efficiently, without any adverse events. 292 (89.8%) patients preferred cloud follow-up for future device management. Conclusion: Compliance with routine in-office visits in this region has much room for improvement. Cloud follow-up addresses the limitations of an imbalanced distribution of follow-up clinics and geographic barriers for in-office CIED evaluation. Thus, cloud follow-up provides a potential solution to the current predicament of routine in-office visits in China.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".