Virtual visits in cardiovascular disease: a rapid review of the evidence
Bibliographic record
Abstract
Given the high prevalence of cardiovascular disease (CVD) in Canada and globally, as well as the staggering cost to human life and health systems, there is an urgent need to understand the successful applications of telemedicine in cardiovascular medicine. While telemedicine in cardiology is well documented, reports on virtual care in the form of synchronous, real-time communication between healthcare providers and patients are limited. As a result of the immediate suspension of ambulatory services for cardiology in Alberta, Canada, due to the Coronavirus Disease 2019 pandemic, we undertook a rapid review on the impact of non-virtual visits in cardiovascular ambulatory settings on patients' healthcare utilization and mortality. Evidence from 12 randomized control trials and 7 systematic reviews was included in the rapid review, with the majority of papers (n = 15) focusing on telemedicine in heart failure. Based on our appraisal of evidence from the last 5 years, virtual visits are non-inferior, or more effective, in reducing hospitalizations and visits to emergency departments in patients with CVD compared to traditional standard in-clinic/ambulatory care. The evidence for a superior effect of virtual visits in reducing mortality was not supported in this review. While telemedicine is an appropriate tool for CVD follow-up care, more research into the efficacy of different components of telemedicine and virtual visits is required.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".