ClinicalTrials.gov
public full text
Public Domain
Randomized Controlled Evaluation of Robotic Cardiac Surgery Training Modalities
The purpose of this research is to objectively compare various training modalities currently available in minimally invasive and robotic surgery to determine the most effective training methods specific to robotic cardiac surgery. We hope to recruit medical trainees with minimal robotic surgery training experience, randomly assigned to one of three groups (Dry wab, da Vinci Simulator and Wet lab). Trainees will be evaluated at baseline for a standardized tasks common in robotic cardiac surgery and then divided into one of the three training modalities. Trainees will be given sufficient time to become proficient with a variety of predetermined tasks unique to each training group. The trainees will then undergo a reevaluation with the cardiac surgery specific tasks at the end of a standard training period. We will then be able to compare our three groups to show if any have benefits over the others in regards to improvement in technical ability, understanding of the robots functions, efficacy of training, speed of technical skill development, accuracy and efficiency of movement. We will rate each modality on their reproducibility, accuracy of representation, and ease of set up and perform a cost analysis for each of the training methods.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- External Validation of AI-Aided Weaning Software Using Multicenter Retrospective Data#914020
- Effects of Atrial Rate-adaptive Pacing on Exercise Capacity in Patients With Chronic Heart Failure Complicated by Chronotropic Incompetence#895929
- AI-Based Prediction of Difficult Airway in Bariatric Surgery#861079
- Predicting the unpredictable: a novel application of artificial intelligence in the cardiac intensive care unit.#646736
- Official Review · ICLR 2025#787132
Record · ID 848550
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive