Objective:A microsurgical simulation training device based on real clinical scenes was designed and its effectiveness was tested.Methods:From January 1, 2020 to January 1, 2023, postgraduate students in the Plastic and Reconstructive Surgery Department of the First Medical Center of PLA General Hospital and the Plastic Surgery Hospital of Chinese Academy of Medical Sciences were enrolled in this prospective study. The simulation training device consists of four parts: (1)Blood perfusion system, which is used to simulate living animal blood vessels.(2)The inner baffling rod system, which is used to simulate the operation in deep cavity.(3) The exterior baffling rod system, which is used to simulate the operation in difficult positions.(4) A pulsating platform system is used to simulate microsurgery under the influence of respiratory movement. Preliminary verification of the effect of the simulated training device was as follows: Surgeons with no experience in microsurgery were completely randomized assigned to the control group (traditional microsurgery training group) and the experimental group (training group using the simulated training device). After 4 weeks of microsurgical training, the trainees were assigned to perform two surgical skill assessments, the first using a live animal model for end-to-end anastomosis of rat tail arteries, and the second assessment using end-to-end anastomosis of free latissimus dorsi flap arteries in a real case. The performance of the two groups was compared by using operation time and microsurgical GRS score scale including four items of dexterity, visuospatial ability, operative flow and judgment. Chi-squared test was used to analyze gender between the two groups. GRS scores between the two groups were compared by the Mann-Whitney U test. Participants’ ageand operation time between the two groups was compared by independent t-test. P<0.05 was considered statistically significant. Results:A total of 18 trainees were enrolled, including 10 in the control group, 6 males and 4 females, with an average age of (27.80±1.87) years. There were 8 subjects in the experimental group, 4 males and 4 females, with an average age of (28.10±1.56) years old. There were no significant differences in age, gender and other baseline characteristics between the two groups ( P>0.05). There was no significant difference in GRS score and operation time between the control group and the experimental group ( P> 0.05) in the first assessment. However, in the second assessment of real cases, the GRS score of the experimental group was significantly higher than that of the control group(14.25 vs. 5.70), and the operation duration of the experimental group was also shorter than that of the control group, and the difference was statistically significant[(100.37±24.65 ) min vs. (105.60±22.84) min] ( P<0.05). Conclusion:Compared with traditional microsurgery training methods, using microsurgery training devices based on clinical real scenes can effectively shorten the learning curve and enable trainees to master complex micromanipulation skills more quickly.

Objective:A microsurgical simulation training device based on real clinical scenes was designed and its effectiveness was tested.Methods:From January 1, 2020 to January 1, 2023, postgraduate students in the Plastic and Reconstructive Surgery Department of the First Medical Center of PLA General Hospital and the Plastic Surgery Hospital of Chinese Academy of Medical Sciences were enrolled in this prospective study. The simulation training device consists of four parts: (1)Blood perfusion system, which is used to simulate living animal blood vessels.(2)The inner baffling rod system, which is used to simulate the operation in deep cavity.(3) The exterior baffling rod system, which is used to simulate the operation in difficult positions.(4) A pulsating platform system is used to simulate microsurgery under the influence of respiratory movement. Preliminary verification of the effect of the simulated training device was as follows: Surgeons with no experience in microsurgery were completely randomized assigned to the control group (traditional microsurgery training group) and the experimental group (training group using the simulated training device). After 4 weeks of microsurgical training, the trainees were assigned to perform two surgical skill assessments, the first using a live animal model for end-to-end anastomosis of rat tail arteries, and the second assessment using end-to-end anastomosis of free latissimus dorsi flap arteries in a real case. The performance of the two groups was compared by using operation time and microsurgical GRS score scale including four items of dexterity, visuospatial ability, operative flow and judgment. Chi-squared test was used to analyze gender between the two groups. GRS scores between the two groups were compared by the Mann-Whitney U test. Participants’ ageand operation time between the two groups was compared by independent t-test. P<0.05 was considered statistically significant. Results:A total of 18 trainees were enrolled, including 10 in the control group, 6 males and 4 females, with an average age of (27.80±1.87) years. There were 8 subjects in the experimental group, 4 males and 4 females, with an average age of (28.10±1.56) years old. There were no significant differences in age, gender and other baseline characteristics between the two groups ( P>0.05). There was no significant difference in GRS score and operation time between the control group and the experimental group ( P> 0.05) in the first assessment. However, in the second assessment of real cases, the GRS score of the experimental group was significantly higher than that of the control group(14.25 vs. 5.70), and the operation duration of the experimental group was also shorter than that of the control group, and the difference was statistically significant[(100.37±24.65 ) min vs. (105.60±22.84) min] ( P<0.05). Conclusion:Compared with traditional microsurgery training methods, using microsurgery training devices based on clinical real scenes can effectively shorten the learning curve and enable trainees to master complex micromanipulation skills more quickly.

In 2010, the Asian Society of Cardiovascular Imaging (ASCI) provided recommendations for cardiac CT and MRI, and this document reflects an update of the 2010 ASCI appropriate use criteria (AUC). In 2016, the ASCI formed a new working group for revision of AUC for noninvasive cardiac imaging. A major change that we made in this document is the rating of various noninvasive tests (exercise electrocardiogram, echocardiography, positron emission tomography, single-photon emission computed tomography, radionuclide imaging, cardiac magnetic resonance, and cardiac computed tomography/angiography), compared side by side for their applications in various clinical scenarios. Ninety-five clinical scenarios were developed from eight selected pre-existing guidelines and classified into four sections as follows: 1) detection of coronary artery disease, symptomatic or asymptomatic; 2) cardiac evaluation in various clinical scenarios; 3) use of imaging modality according to prior testing; and 4) evaluation of cardiac structure and function. The clinical scenarios were scored by a separate rating committee on a scale of 1–9 to designate appropriate use, uncertain use, or inappropriate use according to a modified Delphi method. Overall, the AUC ratings for CT were higher than those of previous guidelines. These new AUC provide guidance for clinicians choosing among available testing modalities for various cardiac diseases and are also unique, given that most previous AUC for noninvasive imaging include only one imaging technique. As cardiac imaging is multimodal in nature, we believe that these AUC will be more useful for clinical decision making.
Area Under Curve ; Asian Continental Ancestry Group* ; Clinical Decision-Making ; Consensus* ; Coronary Artery Disease ; Echocardiography ; Electrocardiography ; Heart Diseases ; Humans ; Magnetic Resonance Imaging ; Methods ; Positron-Emission Tomography ; Radionuclide Imaging ; Tomography, Emission-Computed