Telemedicine and Remote Rehabilitation for Patients with Cardiac Disease Post COVID-19 Era
Bibliographic record
Abstract
Background The COVID-19 pandemic has accelerated the changes of daily practice in medical care worldwide. Telemedicine is one of the most stimulated fields among these changes, even in the cardiovascular disease care. In the past decade, remote monitoring has become an important tool that provides valuable clinical information via respiratory assist devices and implantable pacemakers from patients at home. Objective The objective of our study was to investigate whether these remote monitoring approaches could include a broader spectrum of patients with cardiac disease with the development of noninvasive monitoring devices. Methods Several pilot studies of telemedicine-based monitoring systems using commercially available digital devices were conducted in the infectious disease wards and other clinical settings during the COVID-19 pandemic. We evaluated the effectiveness of a remote heart monitoring system that provides real-time electrocardiogram and other vital signs monitoring in patients with cardiovascular disease. A newly developed remote cardiac rehabilitation system was also evaluated. Results We found that current remote monitoring technology could provide sufficient monitoring of vital signs, suggesting a potential to predict a worsening of heart failure in advance. Remote cardiac rehabilitation could be effectively and safely provided in patients with low to medium risk. Conclusions Telemedicine and remote cardiac rehabilitation possess a great potential in the cardiovascular disease practice post COVID-19 era; however, there are several unsolved issues regarding their implementation in the real-world clinical practice.
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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) | 0.000 | 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) | 0.000 | 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".