Implementation of telemedicine in the care of patients with aortic dissection
Bibliographic record
Abstract
Telemedicine uses telephone-based or any form of digital communication for remote clinical services. It has been a field of interest for the last century, with broader implementation of telemedicine technologies during the last 25 years. The COVID-19 pandemic was an impetus for the adoption of these technologies globally across all health care services, including patient care, surgical practice, and workflow. As part of the patient engagement work in the Aortic Dissection Collaborative, this topic was identified as an important patient-centered research topic. Telemedicine has been adopted increasingly in vascular surgery; however, there is little evidence on appropriate use of these technologies pertaining to treating patients with aortic dissection or aortopathy in general. This landscape review summarizes the uses of telemedicine applications pre and post pandemic in medicine and vascular surgery, with a particular focus on uses in aortopathy. Using common resource databases, we identified articles related to the history of telemedicine, its current utilization, and application to vascular surgery and/or aortopathy. We briefly review the history of telemedicine and illustrate a range of applications in medicine before the pandemic, along with its rapid uptake globally during the COVID-19 pandemic. The enablers and barriers to using telemedicine are explored, although as a whole there is satisfaction with its integration among patients and providers. To address these, we offer recommendations to address future research as it pertains to telemedicine technologies in aortic dissection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".