View clinical trials related to Surgery.
Filter by:The VERDICT-2 user testing study will user test the Cloud DX Vitaliti Continuous Vital Signs Monitor (Model: CVSM-1A) in an ambulatory context with post-surgical participants, their family members (caregiver support role) and clinicians (nurses and physicians) at Hamilton Health Sciences to examine user acceptance and user experiences, while soliciting user feedback on wearability compliance and proposed clinical workflows.
While blood clots after major cancer surgery are common and harmful to patients, the medications to decrease blood clot risk are seldom used after patients leave the hospital despite the recommendation of multiple professional medical societies. The reason why these medications are seldom prescribed is not well understood. The main questions this study aims to answer are: - Does surgeon education paired with an electronic medical record based decision support tool improve the guideline concordant prescription of pharmacologic venous thromboembolism after abdominopelvic cancer surgery? - Does dedicated patient education regarding blood clots at the time of hospital discharge after abdominopelvic cancer surgery improve understanding of the risk of venous thromboembolism and adherence to pharmacologic prophylaxis? The investigators will study these questions using a stepped-wedge randomized trial where groups of surgeons will use a tool integrated to the electronic medical record to educate them on the individualized patient risks of blood clots after major cancer surgery and inform them regarding guidelines for preventative medicines. Utilization of the medications before and after using the tool will be compared. Patients will be administered a questionnaire assessing their awareness of blood clots as a risk after cancer surgery. For those prescribed medications to reduce blood clot risk after leaving the hospital, the questionnaire will evaluate whether they took the medications as prescribed. Survey results will be evaluated before and after implementation of education on blood clot risk at the time of hospital discharge.
The aim of this study was to compare the accuracy of ctDNA-MRD longitudinal surveillance model and internationally accepted pathological MVI results in predicting recurrence after radical hepatectomy. At the same time, to explore the relationship among the two methods of predicting recurrence of hepatocellular carcinoma, postoperative adjuvant therapy and postoperative recurrence, this study further confirmed the effectiveness of ctDNA-MRD longitudinal monitoring model in monitoring postoperative recurrence of hepatocellular carcinoma and guiding treatment.
The aim of this single-centre prospective randomized-controlled clinical trial is to assess whether patients adhere to prescribed weight bearing limits after surgical orthopaedic or traumatological interventions more accurately after instruction using a biofeedback method than using the standard method.
A novel device that works with robotic trocars to clean the scope when visualization is compromised during a surgical procedure.
The incidence of postoperative delirium in elderly patients is high, which can lead to long-term postoperative neurocognitive disorders. Its high risk factors are not yet clear. At present, there is a lack of early diagnosis and alarm technology for perioperative neurocognitive disorders, which can not achieve early intervention and effective treatment. By artificial intelligence and autonomously evolutionary neural network algorithm, relying on multi-source clinical big data, we explored the use of Bayesian network to optimize the anesthesia decision-making system in enhanced recovery after surgery, and established risk prediction model for perioperative critical events. It is expected that this method will also help to establish a risk prediction model for postoperative delirium and long-term postoperative neurocognitive disorders. This project plans to collect the perioperative sensitive parameters of anesthesia machine, multi-parameter monitor, EEG monitor,fMRI and HIS system, to explore the evolution process of data characteristics by feature fusion.We also plan to quickly screen key perioperative risk characteristics of postoperative delirium from massive clinical data through feature selection, to explore the high risk factors of long-term postoperative neurocognitive disorders developing from postoperative delirium. Finally, with multi-center intelligent analysis，the risk prediction model of postoperative delirium and long-term postoperative neurocognitive disorders will be constructed.
The goal of this clinical trial is to learn if the artificial intelligence technology helps to improve the efficiency in robot assited spinal surgery. The main questions it aims to answer are: Does the AI technology shorter the mannual planning time of screw trajectories？ Does the AI technology affect the surgical accuracy？ Researchers will compare the artificial intelligence technology to the conventional mannual planning in robotic surgery. Participants who met inclusion criteria and do not have any exclusion criterion will be randomized to artificial intelligence or mannual planning group.
The goal of this pilot study is to learn if a class and hands-on-practice of ergonomic body positions - or specific ways to move the body while working to prevent injury - is valuable to training obstetrics and gynecology doctors. The main questions the study team aims to answer are: - Will these lessons successfully teach the participants how to move bodies at work in a way that will prevent injury? - Will the participants feel that learning and practicing such lessons helps to avoid injury while at work? Researchers will compare training obstetrics and gynecology doctors that attend a class on ergonomics and have guided hands-on-practice of ergonomic body positions with training obstetrics and gynecology doctors that attend the class only to see if the first group learns and remembers how to move their bodies safely while working. All participants will attend a class that teaches basic ergonomic lessons before they are divided into two groups. Group 1 will practice common surgery skills on a model while being videotaped by an artificial intelligence application. The application will make a report on unsafe positions a participant does while practicing surgical skills. The Group 1 participant will then go over the report with one of the study supervisors to talk about ways that the participant can move safely while practicing the skills. The participant will then practice the skills one more time while being videotaped. The study supervisors will then compare the two reports to see if the participant improved. Group 2 will also practice common surgery skills on a model while being videotaped. Group 2 participants will not get to see the report that the application generates or speak with the study supervisors about ways to move safely while practicing the skills. There will be a follow up after two months to see if participants remembered what was learned during the class and during the hands-on practice lesson. All participants will again be videotaped. The study supervisors will compare the videos and reports from the last class to the most recent ones to see if the participants learned and remember how to move safely while working. Participants in both groups will take a quiz about the lessons learned in the class before and after the class to determine what had been learned from the lesson. A survey about how useful and helpful the class was and hands-on practice sessions were will also be completed.
Given the scarcity of studies aimed at assessing the effect of anesthesia and m ventilation on the distribution of lung ventilation in pediatric patients undergoing surgery, with the exclusion of thoracic surgery, the present prospective observational study would shed the light on ventilation practice in pediatric anesthesia for surgery. This study wold fill the actual gap allowing the evaluation, through electrical impedance tomography (EIT) of the distribution of lung ventilation across the different phases of anesthesia for pediatric surgery. These insights could contribute to improve clinical practice and research in the management of ventilation in pediatric patients undergoing anesthesia for surgery.
This trial aims to demonstrate that the Neuralert Monitoring System will detect strokes before they would be identified by current standard of care. Each patient will be monitored for up to five days, depending on device connectivity or battery duration. Each monitoring session will consist of wearing a Neuralert device on each arm. For this pilot trial, we are interested in learning about Wi-Fi connectivity, successful data transmission, clinical usability, and tolerability.