The Impact of Simulation-Based Training in Cardiovascular Medicine: A Systematic Review
Bibliographic record
Abstract
Cardiovascular medicine and practice in recent times have evolved as complex procedures are performed to manage difficult cases. The majority of these interventions are done percutaneously in order to minimize patient risk. Additionally, training specialist in handling these interventions require a lot of exposure to them; as such, patients are at higher risk of errors and complications from trainees before attaining expertise. In order to avoid these possible risks to patients and ensure their safety, using simulation commonly in cardiovascular specialist education is a possible trend in the future. This article aims to review randomized controlled trials that were performed in cardiology and vascular medicine regarding the use of simulating models to transfer skills to trainees. This study is a systematic review that includes publications dated from 2010 from any country and only in English. The search involved several combinations of search terms from medical subject headings (MeSH). Keywords in the title, abstract, and text for the population, intervention, control, and outcomes were first done in a pilot search to establish the sensitivity of the search strategy. Studies were searched in PubMed, Medline, Cochrane Library, Embase, CINAHL, and Hirani. Data were presented in the PRISMA flowchart and tabular form. A total of 389 studies were obtained from five databases using the search strategies. Eighty-nine studies were excluded for duplication. The total number of studies that did not meet the inclusion criteria was 269, and they were excluded based on abstract and title screening. Another 18 studies were excluded based on full-text screening. In this study, 13 articles were selected ranging from 2011 to 2022. The majority of the outcomes of the study demonstrated that simulation tutoring complements traditional methods of training. Countries of publication were the United States of America, Canada, Italy, Korea, California, Ireland, Germany, Belgium, Switzerland, United Kingdom, Netherlands, and France. Procedures simulated include coronary angiography, transseptal catheterization, cardiopulmonary resuscitation, ultrasound-guided radial artery cannulation, diagnostic angiograms, coiled carotid terminus aneurysms in the setting of subarachnoid hemorrhage, middle cerebral artery embolectomies, renal artery angioplasty/stenting, endovascular aneurysm repair, transvenous pacing wire, intra-aortic balloon pump, and pericardiocentesis. Despite the accredited drawback of availability and cost noted with simulation-based education, there is evidence that it offers many advantages compared to traditional teaching methods. From this study, simulation-based teaching has been shown to effectively transfer skills to trainees especially when used as an adjunct to the apprenticeship method. As a result, we recommend that virtual reality education should be integrated with real-life teaching in modern cardiovascular modules as this will help ensure early skill transfer while maintaining patient safety.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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".