Evaluating the Effectiveness of High-Fidelity Simulation-Based Training on Clinical Skill Transfer in Obstetric Ultrasound Residents: A Prospective Randomized Controlled Trial

Evaluating the Effectiveness of High-Fidelity Simulation-Based Training on Clinical Skill Transfer in Obstetric Ultrasound Residents: A Prospective Randomized Controlled Trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the Effectiveness of High-Fidelity Simulation-Based Training on Clinical Skill Transfer in Obstetric Ultrasound Residents: A Prospective Randomized Controlled Trial Jinguang Zhou, Yongfeng Zhao, Ping Zhou, Xiaohong Tang, Jie Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7132399/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Feb, 2026 Read the published version in BMC Medical Education → Version 1 posted 10 You are reading this latest preprint version Abstract Background Traditional training in obstetric ultrasound is constrained by patient availability and ethical concerns. In contrast, simulation-based medical education offers a safe, repeatable, and realistic alternative by replicating clinical environments for hands-on practice. This study aims to evaluate the clinical transferability of skills acquired through simulation and compare its effectiveness with traditional training in improving residents’ ultrasound competencies. Methods This prospective randomized controlled study included 22 residents, randomly assigned to either the simulator (n = 11) or traditional (n = 11) group. All participants underwent short-term mid-trimester fetal ultrasound training, using either a simulator or real pregnant women. Participants were required to perform assessments on both the simulator and volunteer pregnant women before and after training. Assessment scores were evaluated by ultrasound consultants using Objective Ultrasound Competency Assessment Tool. Results After training, both groups showed significant improvement in post-test scores (p < 0.05). Trainees in the simulator group not only demonstrated enhanced assessment scores in the simulated environment(57.7 ± 11.0vs81.0 ± 5.6, p < 0.01) but also exhibited notable improvements in their skills in the real clinical setting(53.5 ± 8.8vs74.1 ± 12.0, p < 0.01). A comparison of the assessment scores between the two groups revealed that the type of training had a significant impact on the residents' performance improvement, with simulator-based training proving to be more effective than traditional training(p 0.14). Pearson correlation analysis indicated a strong correlation between the simulator assessment scores and the pregnant women assessment scores (r > 0.7). Conclusions Simulation-Based Medical Education enhances residents' performance in both simulated environments and real clinical settings and is more effective than traditional clinical training methods. Additionally, simulation can serve as a powerful complement to traditional training and a safe alternative to patient-based assessment methods involving pregnant women. Trial registration This trial was registered at the Chinese Clinical Trial Registry with registration number ChiCTR2500107408 on August 11, 2025 (Retrospectively registered). ultrasound Obstetric simulation simulation based medical education Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ultrasound plays a crucial role in prenatal diagnosis, providing significant value in evaluating fetal growth and detecting abnormalities 1 . Although the frequency of prenatal ultrasound examinations has increased 2 , their sensitivity in detecting fetal abnormalities remains between 25% and 40% 3,4 . The accuracy of these examinations is heavily influenced by the skill level of the sonographer. Moreover, obstetric ultrasound faces challenges related to the “second patient,” which refers to factors like fetal posture, position, and orientation that can complicate the examination process 5 . Inexperienced practitioners may lead to misdiagnoses, which not only affect patient care but may also increase medical costs, lengthen hospitalization, and even impact treatment outcomes 6 . Traditional training typically follows a master-apprentice model, relying on real patients for teaching. However, this approach faces several challenges, including the risk of excessive fetal exposure due to overuse of prenatal ultrasounds, limited availability of rare cases, and the considerable operational pressures placed on trainees 7 . In contrast, Simulation-Based Medical Education (SBME) serves as an effective supplement to traditional methods, offering several notable advantages: repeatability (allowing skills to be refined through repeated practice in identical scenarios), zero risk (enabling trial and error without clinical consequences), scenario diversity (simulating a broad spectrum of clinical situations, ranging from normal pregnancies to rare complications), personalized learning (tailoring to individual learning paces and styles), and real-time feedback (accelerating the learning process through immediate assessment) 8 , 9 . Given its exceptional educational value, authoritative institutions such as EFSUMB, the WHO (2013), AUBMC, and WFUMB have recommended the integration of SBME into standardized curricula 10 – 13 . Although some scholars remain cautious about the effectiveness of SBME 14 , 15 , an increasing body of research indicates that SBME has yielded positive results in skill training across various fields 16 – 19 . Lee (1995) 20 were the first to report the application of SBME in prenatal diagnosis, and its effectiveness has since been repeatedly validated by a range of subsequent studies 21 – 23 . However, existing research still presents several limitations: 1. Lack of systematization (many studies focus on specific procedural steps, lacking comprehensive training programs and extensive data analysis); 2. Unclear impact on clinical behavior (the influence of simulation-based education on trainees' clinical behavior remains under investigation); 3. Unclear training efficiency (simulation-based training often requires significant financial and human resource investments, and its efficiency compared to traditional training methods still warrants further exploration). We believe that obstetric ultrasound simulation training can effectively and efficiently enhance the skill level of trainees and improve their performance in real clinical environments. To this end, we conducted a systematic educational evaluation focused on routine mid-trimester fetal scanning. The primary objectives of the study are as follows: 1) to explore the effectiveness of transferring skills acquired through simulation training to real clinical abilities; and 2) to compare the improvement in trainees' performance between simulation-based training and traditional training. The secondary objective is to explore the correlation between skill performance in simulated environments and real clinical settings, thereby demonstrating the feasibility of high-fidelity simulators as tools for clinical competency assessment. Materials and methods This study was conducted at the Third Xiangya Hospital of Central South University, from September 2020 to December 2021. The study was approved by the Institutional Review Board of The Third Xiangya Hospital of Central South University (No: 2020-S375). All procedures were carried out in accordance with the Declaration of Helsinki. A randomized controlled trial design was employed, including pre-test and post-test assessments, as shown in Fig. 1 . Participants A total of 22 trainees were enrolled in this study. The inclusion criteria were as follows: PGY-2 and PGY-3 trainees who had completed more than one year of training and had not yet independently conducted routine mid-trimester fetal ultrasound examinations. The exclusion criteria included individuals without any ultrasound knowledge and the inability to operate ultrasound equipment, as well as those who had previously undergone obstetric ultrasound simulation training. All trainees were randomly assigned to two groups: the simulator group (n = 11) and the traditional group (n = 11). The simulator group received simulation-based training, while the traditional group received training with volunteer pregnant women. The volunteer pregnant women were between 20 and 24 weeks of gestation, and fetal abnormalities were excluded in advance. All trainees and volunteer pregnant women signed informed consent forms. Additionally, two consultants with more than five years of independent obstetric ultrasound examination experience were selected as assessors to independently evaluate the performance of the trainees. Simulator equipment This study employed the Simbionix U/S Mentor simulators (Tel Aviv, Israel) for simulation training. The simulator consists of an electronic control box, a sensor table with a mannequin torso, and a touchscreen with divided screens. The split-screen display shows the ultrasound scan image on one side and the buttons, along with an anatomical assistance model, on the other. As trainees move the probe across the mannequin's abdomen, the virtual fetal ultrasound image and 3D anatomical diagrams are displayed in real-time on the screen. All participants selected the same normal fetal module for assessment. This module, through the unique features of the U/S Mentor, presents a randomly positioned, movable fetus. Furthermore, during the assessment, the 3D anatomical diagram is hidden, thereby simulating the conditions of a real clinical environment. Training program Step 1 Preparation All participants underwent 2 hours of theoretical training on routine mid-trimester fetal ultrasound scan. The content covered topics such as pre-examination preparation, the acquisition of standardized ultrasound planes, identification of normal anatomical structures, and proper biometric measurement techniques. Step 2 Pre-test Prior to the skills assessment, all participants will receive 15 minutes of familiarization of the operation of the simulator and ultrasound diagnostic equipment, but this phase will not include any skill practice. Following this, participants in the simulator group and the traditional group will be required to follow the mid-trimester fetal ultrasound examination procedure, performing either the simulator assessment and the volunteer pregnant woman assessment. Each assessment will not exceed 30 minutes, with participants required to independently complete the entire examination process. Step 3 Training In the simulator group, skills training is conducted using the Simbionix U/S Mentor simulators, while the traditional group receives instruction through volunteer pregnant women. The training curriculum is based on the Practice Guidelines for Performance of the Routine Mid-Trimester Fetal Ultrasound Scan 24 and is divided into four distinct task modules (as detailed in the supplementary materials), along with one comprehensive training session. Each training group consists of 3 to 4 participants. Initially, the instructor demonstrates the procedures on either the simulator or a volunteer pregnant woman. Following the demonstration, participants take turns practicing the techniques. To prevent fatigue, each participant's continuous practice time is limited to 30 minutes, with each module's total practice time set to 1 hour, allowing for repeated practice. Each pregnant woman is scanned for no more than 30 minutes to adhere to the ALARA principle 1 , minimizing exposure. After completing their practice, participants need to observe the operations of their peers. Throughout the process, participants have the opportunity to receive guidance and feedback from the instructor. Each task module follows this structure, with the total training duration being 5 hours. Step 4 Post-test The post-test will be conducted within one week following the completion of the training. participants will be required to adhere to the same procedures as in the pre-test, independently completing both the simulator assessment and the volunteer pregnant woman assessment. Assessment tool The Obstetric Ultrasound Competency Assessment Tool (OUCAT), which consists of a total of 123 items, is used to assess participants' pre- and post-test skill levels. This assessment method was developed based on obstetric ultrasound guidelines and has been validated for its effectiveness through prior research 25 . The assessment process will be recorded via high-definition video, and all participant information will be anonymized and assigned a number to ensure that no personal data is included. The assessment videos will then be distributed to two consultants for independent online scoring, and the final score will be the average of the two consultants' ratings. Statistical analysis Statistical analysis was performed using SPSS version 26.0. Continuous variables that followed a normal distribution were presented as mean ± standard deviation, while variables that did not follow a normal distribution were expressed as median and interquartile range. For measurement data, normally distributed data were analyzed using the t-test, and non-normally distributed data were analyzed using the Wilcoxon signed-rank test. Independent samples t-test was used for inter-group comparisons, while paired t-test was employed for intra-group pre- and post-training comparisons. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Analysis of covariance (ANCOVA) was used to assess the effect of different training methods on performance improvement, with partial eta squared (η²) used to evaluate the effect size. The correlation between simulator scores and obstetric scores was assessed using Pearson’s correlation analysis. A p-value of < 0.05 was considered statistically significant. Result The demographic characteristics and pre-test scores of the simulator group and the traditional group are presented in Table 1. There were no statistically significant differences in baseline levels between the two groups, indicating comparability between the groups. Table 1 The demographic and pre-test score analysis of participants in the simulator and traditional groups. Category Simulator group(n = 11) Traditional group(n = 11) p Age 26.6 ± 4.4 27.6 ± 2.9 0.536 Gender 0.311 Male 4 1 Female 7 10 Years of work experience 2(1,2) 2(2,3) 0.333 Years of experience in ultrasound 2(1,5) 2(2,3) 0.76 Years of experience in prenatal ultrasound 1(1,3) 2(1,3) 0.646 Educational background 0.461 Bachelor 6 6 Master 4 2 PhD 1 3 Job title 0.395 Unrated 4 7 Junior 7 4 Pre-test score Simulator assessment 57.7 ± 11.0 58.0 ± 7.9 0.939 Pregnant woman assessment 53.5 ± 8.8 53.3 ± 9.9 0.96 Primary outcome As shown in Fig. 2, a comparison of the pre-test and post-test mean scores was conducted for participants in the simulator and traditional groups. The simulator group’s performance in the simulator assessment increased from 57.7 ± 11.0 to 81.0 ± 5.6 (p < 0.01), while their performance in the pregnant woman assessment improved from 53.5 ± 8.8 to 74.1 ± 12.0 (p < 0.01). In comparison, the traditional group’s performance in the simulator assessment increased from 58.0 ± 7.9 to 67.9 ± 11.7 (p < 0.01), and their performance in the pregnant woman assessment increased from 53.3 ± 9.9 to 62.9 ± 17.0 (p = 0.017). These results are summarized in Table 2. The results indicate that both training methods improved the participants' performance in the routine mid-trimester fetal ultrasound scan assessment. More importantly, SBME not only enhanced participants' performance in simulated environments but also effectively improved their clinical skills in real-world settings. Table 2 The difference in routine mid-trimester fetal ultrasound scanning scores between the simulator and the traditional group after training. Pregnant woman assessment p Simulator assessment p Pre-test Post-test Pre-test Post-test Simulator group 53.5 ± 8.8 74.1 ± 12.0 < 0.01 57.7 ± 11.0 81.0 ± 5.6 < 0.01 Traditional group 53.3 ± 9.9 62.9 ± 17.0 0.017 58.0 ± 7.9 67.9 ± 11.7 < 0.01 Total 53.4 ± 9.1 68.5 ± 15.4 < 0.01 57.9 ± 9.4 74.5 ± 11.2 < 0.01 A covariance analysis of the assessment scores between the two groups indicated (Table 3 and Table 4) that the difference in training methods had a significant effect on the improvement of participants' scores (simulator assessment p 0.14, suggesting a large effect size (simulator assessment Partial η² = 0.459, pregnant woman assessment Partial η² = 0.273). Figure 3 illustrates the improvement slopes between the pre-test and post-test scores for participants in the simulator and traditional groups. These findings suggest that SBME is more effective than traditional training in enhancing participants' performance. Table 3 Analysis of Covariance for Training Methods Effects on Performance Gains, Adjusted for Pre-test Variability - simulator assessment Variable Adjusted Mean(SE) 95% CI F(1,19) p Partial η² Training Method 16.1 < 0.001 0.459 Simulator 81.132(2.345) [76.223,86.041] Pregnant woman 67.823(2.345) [62.913,72.732] Covariate Pre-test score 8.9 0.008 0.320 Table 4 Analysis of Covariance for Training Methods Effects on Performance Gains, Adjusted for Pre-test Variability – pregnant woman assessment Variable Adjusted Mean(SE) 95% CI F(1,19) p Partial η² Training Method 7.1 0.015 0.273 Simulator 73.990(2.892) [67.936,80.043] Pregnant woman 63.056(2.892) [57.002,69.110] Covariate Pre-test score 27.9 < 0.001 0.595 Secondary outcome An overall analysis of the two groups revealed that participants' assessment scores on the simulator were significantly higher than those on the pregnant woman assessment (pre-test: 57.9 ± 9.4 vs. 53.4 ± 9.1, p < 0.001; post-test: 74.5 ± 11.2 vs. 68.5 ± 15.4, p = 0.007) (Table 5). Additionally, Pearson correlation analysis (Fig. 4) showed a strong correlation between the two assessment scores (pre-test: r = 0.8385, post-test: r = 0.7958). These results indicate that participants performed better in the simulator assessment than in the pregnant woman assessment, and the simulator assessment was able to accurately reflect their ability to examine real pregnant women. Table 5 Comparison and Correlation Analysis of Trainees' Simulator and Pregnant woman Assessment Scores Simulator assessment Pregnant woman assessment t p(t-test) Pearson r p (Pearson r) Pre-test 57.9 ± 9.4 53.4 ± 9.1 -3.995 < 0.001 0.8385 < 0.001 Post-test 74.5 ± 11.2 68.5 ± 15.4 -2.969 0.007 0.7958 < 0.001 Discussion Ideally, every time a new training method is implemented, it should generate a series of impacts on several levels. The Kirkpatrick model is the most commonly used evaluation framework, measuring the effectiveness of training and its practical outcomes through a multi-dimensional assessment system 26 . However, most of the current studies on obstetric ultrasound simulation mainly remain at the first and second levels of the Kirkpatrick model, with a lack of evaluation on the transfer of knowledge gained during simulation training to clinical practice 27 . To the best of our knowledge, this is the first prospective controlled study to systematically assess obstetric ultrasound simulation training. We found that simulation training effectively improved trainees' obstetric ultrasound skills and was more efficient than traditional clinical training methods. More importantly, this study validated the potential for transferring the knowledge and skills acquired through simulation training into clinical operational competence, with a preliminary extension to the third level (behavior level) of the Kirkpatrick model. Through a comparative analysis of scores obtained from simulator assessments and pregnant women assessments, we further found a high correlation between the two assessment methods, thereby demonstrating the feasibility of simulators as an alternative for assessing residents' clinical competence. Several studies have shown that SBME can effectively improve trainees' obstetric ultrasound performance. However, previous training has mainly focused on certain specific aspects 28 – 31 , such as NT, CRL, BPD, and other fetal biometric measurements. Among them, the studies by Rosen (2017) 28 , Andreasen (2020) 29 , and Grandjean (2021) 30 compared the pre- and post-training assessment scores on real pregnant women, with significant improvements in the post-assessment scores. These findings further substantiate the effectiveness of SBME in improving clinical practice skills among trainees. This is consistent with the results of our study. We conducted a systematic training for trainees on routine mid-trimester fetal ultrasound examination in a simulated environment. A comparison of pre- and post-assessment scores of real pregnant women revealed a significant improvement in the trainees' performance. This further validates the ability of SBME to effectively transfer into clinical practice. Furthermore, when comparing the assessment scores between the simulator group and the traditional group, we found that simulator-based training was more effective in improving trainee scores than traditional training.We attribute this to the advantages of simulators, such as repeatability, zero risk, diverse scenarios, personalized learning, and real-time feedback. Research by Rosen (2017) 28 and Grandjean (2021) 30 also compared the training effectiveness of SBME with traditional training and found that SBME was equally effective as clinical training, with beginners who had weaker foundational knowledge showing more significant learning gains in simulator-based training. We believe that this difference may be due to variations in the baseline levels of trainees in different experiments. Competence assessment is typically based on the completion of a required number of clinical cases 32 , 33 , but minimum case standards vary across regions and organizations 34 – 36 . Additionally, Learning curves differ among trainees and assessment types 37 – 39 ,meaning that completing the minimum number of cases does not necessarily indicate mastery of skills 40 .These differences highlight the importance of the philosophy of Competency-Based Medical Education (CBME),which advocates for the direct observation and assessment of learner' skills 41 . However, due to the unique nature of obstetric ultrasound, implementing assessment often requires the support of volunteer pregnant women, which presents various challenges, including difficulties in large-scale implementation and the complexities of management and coordination. Existing studies indicate that simulators are effective in differentiating the ability levels of trainees in terms of accuracy, speed, and other factors 42 , 43 .This study found a strong correlation between the performance in simulator assessment and in pregnant woman assessment. This findings is consistent with the studies of Chalouhi (2016) 44 , Corroenne (2023) 45 and ATHIEL (2025) 46 . It further confirms that simulators can serve as a reliable alternative to real-life assessments of pregnant women, offering a trusted tool for evaluating trainee competency. The results of this study also found that simulator test scores were significantly higher than those of the obstetric test, in agreement with the findings of Chalouhi (2016) 44 . Therefore, we suggest that when applying simulator assessments, more stringent minimum passing standards should be established to effectively bridge the gap between simulator and pregnant woman assessments, ensuring that trainees possess the practical operational skills required in clinical settings. We acknowledge certain limitations in this study. Firstly, the sample size is relatively small, and some analyses may not be sufficiently robust. However, statistically significant effects were observed in the results, providing a foundation for further large-scale research. If these findings can be replicated in clinical settings, they would have significant clinical implications. Although the methods and training processes employed in this study are highly replicable and can be easily implemented in other institutions, the data collection was limited to a single center, which somewhat restricts the generalizability of the results. But we believe there are no significant differences between the participants in this study and those from other institutions. Furthermore, this study demonstrated the short-term effectiveness of SBME in enhancing learners' skills, but there is a lack of long-term follow-up data to assess its impact on the sustainability of these skills. While SBME can’t replace traditional training methods, it can serve as an effective supplement for learner training and assessment. Additionally, further research is needed to explore the potential of virtual reality simulations, in order to better understand their role in skill development and innovation in educational models. Conclusion This prospective controlled study evaluates the effectiveness of SBME in obstetric ultrasound skill training and highlights its significant advantages in enhancing clinical competencies among trainees. The results demonstrate that SBME not only improves performance within simulated environments but also facilitates the effective transfer of knowledge and skills from simulation to real clinical practice. Simulation can serve as a powerful complement to traditional training and an effective alternative to assessment methods based on pregnant woman. SBME offers considerable practical value and holds promise for playing an increasingly crucial role in the future development of educational models. Declarations Acknowledgements None. Authors' contributions Y. Zhao designed the study and organized the research project. J. Zhou analyzed the data and wrote the manuscript. P. Zhou. Dr. Xiaohong Tang revised the study design and the manuscript. S. Guo and L. Li trained and rated the participants. W. Liu and J. Wang collected assessment videos and data. All authors approved the submission of the manuscript. Funding Project of the 14th five year plan of Educational Science in Hunan Province (XJK21AGD002). Data availability The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board of The Third Xiangya Hospital of Central South University. (No: 2020-S375). All participants signed informed consent. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 Department of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha, China; 2 Clinical skill center, The Third Xiangya Hospital of Central South University, Changsha, China; 3 De p artment of Ultrasound, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China References Salomon LJ, Alfirevic Z, Berghella V, et al. ISUOG Practice Guidelines (updated): performance of the routine mid‐trimester fetal ultrasound scan. Ultrasound in Obstetrics & Gynecology . 2022;59(6):840-856. doi:10.1002/uog.24888 O'Keeffe DF, Abuhamad A. Obstetric ultrasound utilization in the United States: Data from various health plans. Seminars in Perinatology . 2013;37(5):292-294. doi:10.1053/j.semperi.2013.06.003 Schmand C, Misselwitz B, Hudel H, et al. Analysis of the Results of Sonographic Screening Examinations According to the Maternity Guidelines Before and After the Introduction of the Extended Basic Screening (IIb Screening) in Hesse. 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Ultrasound in Obstetrics & Gynecology . 2010;37(1):116-126. doi:10.1002/uog.8831 Zhao Y, Zhou P, Zhu W, et al. Validity evidence for simulator-based obstetric ultrasound competency assessment tool: a multi-center study. Ultraschall in der Medizin - European Journal of Ultrasound . 2023;45(02):168-175. doi:10.1055/a-2122-6746 Frye AW, Hemmer PA. Program evaluation models and related theories: AMEE Guide No. 67. Medical Teacher . 2012;34(5):e288-e299. doi:10.3109/0142159x.2012.668637 Hani S, Chalouhi G, Lakissian Z, Sharara-Chami R. Introduction of Ultrasound Simulation in Medical Education: Exploratory Study. JMIR Medical Education . 2019;5(2)doi:10.2196/13568 Rosen H, Windrim R, Lee YM, Gotha L, Perelman V, Ronzoni S. Simulator Based Obstetric Ultrasound Training: A Prospective, Randomized Single-Blinded Study. Journal of Obstetrics and Gynaecology Canada . 2017;39(3):166-173. doi:10.1016/j.jogc.2016.10.009 Andreasen LA, Tabor A, Nørgaard LN, et al. Multicenter randomized trial exploring effects of simulation‐based ultrasound training on obstetricians' diagnostic accuracy: value for experienced operators. Ultrasound in Obstetrics & Gynecology . 2020;55(4):523-529. doi:10.1002/uog.20362 Grandjean GA, Bertholdt C, Zuily S, et al. Fetal biometry in ultrasound: A new approach to assess the long-term impact of simulation on learning patterns. Journal of Gynecology Obstetrics and Human Reproduction . 2021;50(8)doi:10.1016/j.jogoh.2021.102135 Leonardi M, Murji A, D'Souza R. Ultrasound curricula in obstetrics and gynecology training programs. Ultrasound in Obstetrics & Gynecology . 2018;52(2):147-150. doi:10.1002/uog.18978 Hertzberg BS, Kliewer MA, Bowie JD, et al. Physician training requirements in sonography: how many cases are needed for competence? AJR Am J Roentgenol . May 2000;174(5):1221-7. doi:10.2214/ajr.174.5.1741221 Alrahmani L, Codsi E, Borowski KS. The Current State of Ultrasound Training in Obstetrics and Gynecology Residency Programs. Journal of Ultrasound in Medicine . 2018;37(9):2201-2207. doi:10.1002/jum.14570 Salvesen KÅ, Lees C, Tutschek B. Basic European ultrasound training in obstetrics and gynecology: where are we and where do we go from here? Ultrasound in Obstetrics & Gynecology . 2010;36(5):525-529. doi:10.1002/uog.8851 Lee W, Hodges AN, Williams S, Vettraino IM, McNie B. Fetal Ultrasound Training for Obstetrics and Gynecology Residents. Obstetrics & Gynecology . 2004;103(2):333-338. doi:10.1097/01.AOG.0000109522.51314.5c Gabor P, Dimassi K, Aabakke AJM, Rouveau R, Ami O. Ultrasound training in obstetrics and gynecology in Europe: satisfaction survey. Ultrasound in Obstetrics & Gynecology . 2018;51(4):559-560. doi:10.1002/uog.17560 Tolsgaard MG, Rasmussen MB, Tappert C, et al. Which factors are associated with trainees' confidence in performing obstetric and gynecological ultrasound examinations? Ultrasound in Obstetrics & Gynecology . 2014;43(4):444-451. doi:10.1002/uog.13211 Bazot M, Daraï E, Biau DJ, Ballester M, Dessolle L. Learning curve of transvaginal ultrasound for the diagnosis of endometriomas assessed by the cumulative summation test (LC-CUSUM). Fertility and Sterility . 2011;95(1):301-303. doi:10.1016/j.fertnstert.2010.08.033 Pusic MV, Boutis K, Hatala R, Cook DA. Learning Curves in Health Professions Education. Academic Medicine . 2015;90(8):1034-1042. doi:10.1097/acm.0000000000000681 Tolsgaard MG, Chalouhi GE. Use of ultrasound simulators for assessment of trainee competence: trendy toys or valuable instruments? Ultrasound in Obstetrics & Gynecology . 2018;52(4):424-426. doi:10.1002/uog.19071 Holmboe ES, Sherbino J, Long DM, Swing SR, Frank JR. The role of assessment in competency-based medical education. Med Teach . 2010;32(8):676-82. doi:10.3109/0142159x.2010.500704 Madsen ME, Konge L, Nørgaard LN, et al. Assessment of performance measures and learning curves for use of a virtual-reality ultrasound simulator in transvaginal ultrasound examination. Ultrasound in Obstetrics & Gynecology . 2014;44(6):693-699. doi:10.1002/uog.13400 Dyre L, Nørgaard L, Tabor A, et al. Collecting Validity Evidence for the Assessment of Mastery Learning in Simulation-Based Ultrasound Training. Ultraschall in der Medizin - European Journal of Ultrasound . 2016;37(04):386-392. doi:10.1055/s-0041-107976 Chalouhi GE, Bernardi V, Gueneuc A, Houssin I, Stirnemann JJ, Ville Y. Evaluation of trainees’ ability to perform obstetrical ultrasound using simulation: challenges and opportunities. American Journal of Obstetrics and Gynecology . 2016;214(4):525.e1-525.e8. doi:10.1016/j.ajog.2015.10.932 Corroenne R, Jacquier M, Stirnemann J, Salomon LJ, Ville Y, Chalouhi G. Evaluation of optical positioning ultrasound simulator for assessment of trainee ability in obstetric ultrasound. Ultrasound in Obstetrics & Gynecology . 2023;63(1):115-116. doi:10.1002/uog.26295 Athiel Y, Defrance M, Noble P, et al. Comparative evaluation of two simulation technologies for obstetric ultrasound trainees’ assessment. European Journal of Obstetrics & Gynecology and Reproductive Biology . 2025;306:81-86. doi:10.1016/j.ejogrb.2025.01.014 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx CONSORT2025editablechecklist.docx Cite Share Download PDF Status: Published Journal Publication published 17 Feb, 2026 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 28 Nov, 2025 Reviews received at journal 27 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviewers invited by journal 13 Aug, 2025 Editor assigned by journal 13 Aug, 2025 Editor invited by journal 12 Aug, 2025 Submission checks completed at journal 11 Aug, 2025 First submitted to journal 11 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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SA=simulator assessment)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2ComparisonofPreandPostAssessmentScoresofResidentsPWApregnantwomanassessmentSAsimulatorassessment.png","url":"https://assets-eu.researchsquare.com/files/rs-7132399/v1/d1c24c6dd0287b3fb5eee612.png"},{"id":89563980,"identity":"7f82231e-b967-4462-b88b-13c05bccef9a","added_by":"auto","created_at":"2025-08-21 10:32:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1524250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImprovement slope between the pre- and post-test scores of the simulator and the traditional group (PWA=pregnant woman assessment; SA=simulator assessment)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3ImprovementslopebetweenthepreandposttestscoresofthesimulatorandthetraditionalgroupPWApregnantwomanassessmentSAsimulatorassessment.png","url":"https://assets-eu.researchsquare.com/files/rs-7132399/v1/1510f6330a3f491c47c292e9.png"},{"id":89562380,"identity":"58fea5df-f825-42e8-bdca-04802ebd713a","added_by":"auto","created_at":"2025-08-21 10:24:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":831659,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of Pre- and Post-test scores (a: Pre-test, b: Post-test)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4ComparisonofPreandPosttestscoresaPretestbPosttest.png","url":"https://assets-eu.researchsquare.com/files/rs-7132399/v1/823f3185e314c1d8848d04d6.png"},{"id":103251537,"identity":"ac975fe4-0936-4227-8db2-6e31c16cd0cd","added_by":"auto","created_at":"2026-02-23 16:10:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6328656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7132399/v1/32e31d64-0550-45a0-ba40-92240c8b9da3.pdf"},{"id":89562339,"identity":"f6c89e42-5f92-4a17-bbda-01aff1bf74a2","added_by":"auto","created_at":"2025-08-21 10:24:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29292,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7132399/v1/4df17e207b43afe9ce2244ab.docx"},{"id":89562414,"identity":"f12918d5-d022-4f91-9594-47cd10cdfe8a","added_by":"auto","created_at":"2025-08-21 10:24:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":35245,"visible":true,"origin":"","legend":"","description":"","filename":"CONSORT2025editablechecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-7132399/v1/a49324b3255f9b6442deb209.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating the Effectiveness of High-Fidelity Simulation-Based Training on Clinical Skill Transfer in Obstetric Ultrasound Residents: A Prospective Randomized Controlled Trial","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUltrasound plays a crucial role in prenatal diagnosis, providing significant value in evaluating fetal growth and detecting abnormalities\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Although the frequency of prenatal ultrasound examinations has increased\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, their sensitivity in detecting fetal abnormalities remains between 25% and 40%\u003csup\u003e3,4\u003c/sup\u003e. The accuracy of these examinations is heavily influenced by the skill level of the sonographer. Moreover, obstetric ultrasound faces challenges related to the \u0026ldquo;second patient,\u0026rdquo; which refers to factors like fetal posture, position, and orientation that can complicate the examination process\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Inexperienced practitioners may lead to misdiagnoses, which not only affect patient care but may also increase medical costs, lengthen hospitalization, and even impact treatment outcomes\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTraditional training typically follows a master-apprentice model, relying on real patients for teaching. However, this approach faces several challenges, including the risk of excessive fetal exposure due to overuse of prenatal ultrasounds, limited availability of rare cases, and the considerable operational pressures placed on trainees\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In contrast, Simulation-Based Medical Education (SBME) serves as an effective supplement to traditional methods, offering several notable advantages: repeatability (allowing skills to be refined through repeated practice in identical scenarios), zero risk (enabling trial and error without clinical consequences), scenario diversity (simulating a broad spectrum of clinical situations, ranging from normal pregnancies to rare complications), personalized learning (tailoring to individual learning paces and styles), and real-time feedback (accelerating the learning process through immediate assessment)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Given its exceptional educational value, authoritative institutions such as EFSUMB, the WHO (2013), AUBMC, and WFUMB have recommended the integration of SBME into standardized curricula\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough some scholars remain cautious about the effectiveness of SBME\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, an increasing body of research indicates that SBME has yielded positive results in skill training across various fields\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Lee (1995)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003ewere the first to report the application of SBME in prenatal diagnosis, and its effectiveness has since been repeatedly validated by a range of subsequent studies\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, existing research still presents several limitations: 1. Lack of systematization (many studies focus on specific procedural steps, lacking comprehensive training programs and extensive data analysis); 2. Unclear impact on clinical behavior (the influence of simulation-based education on trainees' clinical behavior remains under investigation); 3. Unclear training efficiency (simulation-based training often requires significant financial and human resource investments, and its efficiency compared to traditional training methods still warrants further exploration).\u003c/p\u003e\u003cp\u003eWe believe that obstetric ultrasound simulation training can effectively and efficiently enhance the skill level of trainees and improve their performance in real clinical environments. To this end, we conducted a systematic educational evaluation focused on routine mid-trimester fetal scanning. The primary objectives of the study are as follows: 1) to explore the effectiveness of transferring skills acquired through simulation training to real clinical abilities; and 2) to compare the improvement in trainees' performance between simulation-based training and traditional training. The secondary objective is to explore the correlation between skill performance in simulated environments and real clinical settings, thereby demonstrating the feasibility of high-fidelity simulators as tools for clinical competency assessment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThis study was conducted at the Third Xiangya Hospital of Central South University, from September 2020 to December 2021. The study was approved by the Institutional Review Board of The Third Xiangya Hospital of Central South University (No: 2020-S375). All procedures were carried out in accordance with the Declaration of Helsinki. A randomized controlled trial design was employed, including pre-test and post-test assessments, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eA total of 22 trainees were enrolled in this study. The inclusion criteria were as follows: PGY-2 and PGY-3 trainees who had completed more than one year of training and had not yet independently conducted routine mid-trimester fetal ultrasound examinations. The exclusion criteria included individuals without any ultrasound knowledge and the inability to operate ultrasound equipment, as well as those who had previously undergone obstetric ultrasound simulation training. All trainees were randomly assigned to two groups: the simulator group (n = 11) and the traditional group (n = 11). The simulator group received simulation-based training, while the traditional group received training with volunteer pregnant women. The volunteer pregnant women were between 20 and 24 weeks of gestation, and fetal abnormalities were excluded in advance. All trainees and volunteer pregnant women signed informed consent forms. Additionally, two consultants with more than five years of independent obstetric ultrasound examination experience were selected as assessors to independently evaluate the performance of the trainees.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSimulator equipment\u003c/h3\u003e\n\u003cp\u003eThis study employed the Simbionix U/S Mentor simulators (Tel Aviv, Israel) for simulation training. The simulator consists of an electronic control box, a sensor table with a mannequin torso, and a touchscreen with divided screens. The split-screen display shows the ultrasound scan image on one side and the buttons, along with an anatomical assistance model, on the other. As trainees move the probe across the mannequin's abdomen, the virtual fetal ultrasound image and 3D anatomical diagrams are displayed in real-time on the screen. All participants selected the same normal fetal module for assessment. This module, through the unique features of the U/S Mentor, presents a randomly positioned, movable fetus. Furthermore, during the assessment, the 3D anatomical diagram is hidden, thereby simulating the conditions of a real clinical environment.\u003c/p\u003e\n\u003ch3\u003eTraining program\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStep 1 Preparation\u003c/h2\u003e\u003cp\u003eAll participants underwent 2 hours of theoretical training on routine mid-trimester fetal ultrasound scan. The content covered topics such as pre-examination preparation, the acquisition of standardized ultrasound planes, identification of normal anatomical structures, and proper biometric measurement techniques.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStep 2 Pre-test\u003c/h3\u003e\n\u003cp\u003ePrior to the skills assessment, all participants will receive 15 minutes of familiarization of the operation of the simulator and ultrasound diagnostic equipment, but this phase will not include any skill practice. Following this, participants in the simulator group and the traditional group will be required to follow the mid-trimester fetal ultrasound examination procedure, performing either the simulator assessment and the volunteer pregnant woman assessment. Each assessment will not exceed 30 minutes, with participants required to independently complete the entire examination process.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStep 3 Training\u003c/h2\u003e\u003cp\u003eIn the simulator group, skills training is conducted using the Simbionix U/S Mentor simulators, while the traditional group receives instruction through volunteer pregnant women. The training curriculum is based on \u003cem\u003ethe Practice Guidelines for Performance of the Routine Mid-Trimester Fetal Ultrasound Scan\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e and is divided into four distinct task modules (as detailed in the supplementary materials), along with one comprehensive training session. Each training group consists of 3 to 4 participants.\u003c/p\u003e\u003cp\u003eInitially, the instructor demonstrates the procedures on either the simulator or a volunteer pregnant woman. Following the demonstration, participants take turns practicing the techniques. To prevent fatigue, each participant's continuous practice time is limited to 30 minutes, with each module's total practice time set to 1 hour, allowing for repeated practice. Each pregnant woman is scanned for no more than 30 minutes to adhere to the ALARA principle\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, minimizing exposure. After completing their practice, participants need to observe the operations of their peers. Throughout the process, participants have the opportunity to receive guidance and feedback from the instructor. Each task module follows this structure, with the total training duration being 5 hours.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStep 4 Post-test\u003c/h3\u003e\n\u003cp\u003eThe post-test will be conducted within one week following the completion of the training. participants will be required to adhere to the same procedures as in the pre-test, independently completing both the simulator assessment and the volunteer pregnant woman assessment.\u003c/p\u003e\n\u003ch3\u003eAssessment tool\u003c/h3\u003e\n\u003cp\u003eThe Obstetric Ultrasound Competency Assessment Tool (OUCAT), which consists of a total of 123 items, is used to assess participants' pre- and post-test skill levels. This assessment method was developed based on obstetric ultrasound guidelines and has been validated for its effectiveness through prior research\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The assessment process will be recorded via high-definition video, and all participant information will be anonymized and assigned a number to ensure that no personal data is included. The assessment videos will then be distributed to two consultants for independent online scoring, and the final score will be the average of the two consultants' ratings.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using SPSS version 26.0. Continuous variables that followed a normal distribution were presented as mean ± standard deviation, while variables that did not follow a normal distribution were expressed as median and interquartile range. For measurement data, normally distributed data were analyzed using the t-test, and non-normally distributed data were analyzed using the Wilcoxon signed-rank test. Independent samples t-test was used for inter-group comparisons, while paired t-test was employed for intra-group pre- and post-training comparisons. Categorical variables were analyzed using the chi-square test or Fisher’s exact test. Analysis of covariance (ANCOVA) was used to assess the effect of different training methods on performance improvement, with partial eta squared (η²) used to evaluate the effect size. The correlation between simulator scores and obstetric scores was assessed using Pearson’s correlation analysis. A p-value of \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eThe demographic characteristics and pre-test scores of the simulator group and the traditional group are presented in Table 1. There were no statistically significant differences in baseline levels between the two groups, indicating comparability between the groups.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe demographic and pre-test score analysis of participants in the simulator and traditional groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSimulator group(n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraditional group(n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of work experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of experience in ultrasound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of experience in prenatal ultrasound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational background\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBachelor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJob title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnrated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-test score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimulator assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePregnant woman assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv\u003ePrimary outcome\u003c/div\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;2, a comparison of the pre-test and post-test mean scores was conducted for participants in the simulator and traditional groups. The simulator group\u0026rsquo;s performance in the simulator assessment increased from 57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0 to 81.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while their performance in the pregnant woman assessment improved from 53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8 to 74.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In comparison, the traditional group\u0026rsquo;s performance in the simulator assessment increased from 58.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9 to 67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and their performance in the pregnant woman assessment increased from 53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 to 62.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0 (p\u0026thinsp;=\u0026thinsp;0.017). These results are summarized in Table\u0026nbsp;2. The results indicate that both training methods improved the participants\u0026apos; performance in the routine mid-trimester fetal ultrasound scan assessment. More importantly, SBME not only enhanced participants\u0026apos; performance in simulated environments but also effectively improved their clinical skills in real-world settings.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe difference in routine mid-trimester fetal ultrasound scanning scores between the simulator and the traditional group after training.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePregnant woman assessment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSimulator assessment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimulator group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraditional group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA covariance analysis of the assessment scores between the two groups indicated (Table 3 \u003cstrong\u003eand\u003c/strong\u003e Table 4) that the difference in training methods had a significant effect on the improvement of participants\u0026apos; scores (simulator assessment p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, pregnant woman assessment p\u0026thinsp;=\u0026thinsp;0.015). Furthermore, the Partial \u0026eta;\u0026sup2; values for both analyses were \u0026gt;\u0026thinsp;0.14, suggesting a large effect size (simulator assessment Partial \u0026eta;\u0026sup2; = 0.459, pregnant woman assessment Partial \u0026eta;\u0026sup2; = 0.273). Figure 3 illustrates the improvement slopes between the pre-test and post-test scores for participants in the simulator and traditional groups. These findings suggest that SBME is more effective than traditional training in enhancing participants\u0026apos; performance.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAnalysis of Covariance for Training Methods Effects on Performance Gains, Adjusted for Pre-test Variability - simulator assessment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted Mean(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF(1,19)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePartial \u0026eta;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining Method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimulator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.132(2.345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[76.223,86.041]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePregnant woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.823(2.345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[62.913,72.732]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCovariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-test score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAnalysis of Covariance for Training Methods Effects on Performance Gains, Adjusted for Pre-test Variability \u0026ndash; pregnant woman assessment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted Mean(SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF(1,19)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePartial \u0026eta;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining Method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimulator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.990(2.892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[67.936,80.043]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePregnant woman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.056(2.892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[57.002,69.110]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCovariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-test score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003eSecondary outcome\u003c/h2\u003e\n\u003cp\u003eAn overall analysis of the two groups revealed that participants\u0026apos; assessment scores on the simulator were significantly higher than those on the pregnant woman assessment (pre-test: 57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4 vs. 53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; post-test: 74.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2 vs. 68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4, p\u0026thinsp;=\u0026thinsp;0.007) (Table\u0026nbsp;5). Additionally, Pearson correlation analysis (Fig.\u0026nbsp;4) showed a strong correlation between the two assessment scores (pre-test: r\u0026thinsp;=\u0026thinsp;0.8385, post-test: r\u0026thinsp;=\u0026thinsp;0.7958). These results indicate that participants performed better in the simulator assessment than in the pregnant woman assessment, and the simulator assessment was able to accurately reflect their ability to examine real pregnant women.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparison and Correlation Analysis of Trainees\u0026apos; Simulator and Pregnant woman Assessment Scores\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSimulator assessment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePregnant woman assessment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep(t-test)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePearson r\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep (Pearson r)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eIdeally, every time a new training method is implemented, it should generate a series of impacts on several levels. The Kirkpatrick model is the most commonly used evaluation framework, measuring the effectiveness of training and its practical outcomes through a multi-dimensional assessment system\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, most of the current studies on obstetric ultrasound simulation mainly remain at the first and second levels of the Kirkpatrick model, with a lack of evaluation on the transfer of knowledge gained during simulation training to clinical practice\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo the best of our knowledge, this is the first prospective controlled study to systematically assess obstetric ultrasound simulation training. We found that simulation training effectively improved trainees' obstetric ultrasound skills and was more efficient than traditional clinical training methods. More importantly, this study validated the potential for transferring the knowledge and skills acquired through simulation training into clinical operational competence, with a preliminary extension to the third level (behavior level) of the Kirkpatrick model. Through a comparative analysis of scores obtained from simulator assessments and pregnant women assessments, we further found a high correlation between the two assessment methods, thereby demonstrating the feasibility of simulators as an alternative for assessing residents' clinical competence.\u003c/p\u003e\u003cp\u003eSeveral studies have shown that SBME can effectively improve trainees' obstetric ultrasound performance. However, previous training has mainly focused on certain specific aspects\u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, such as NT, CRL, BPD, and other fetal biometric measurements. Among them, the studies by Rosen (2017)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, Andreasen (2020)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and Grandjean (2021)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e compared the pre- and post-training assessment scores on real pregnant women, with significant improvements in the post-assessment scores. These findings further substantiate the effectiveness of SBME in improving clinical practice skills among trainees. This is consistent with the results of our study. We conducted a systematic training for trainees on routine mid-trimester fetal ultrasound examination in a simulated environment. A comparison of pre- and post-assessment scores of real pregnant women revealed a significant improvement in the trainees' performance. This further validates the ability of SBME to effectively transfer into clinical practice. Furthermore, when comparing the assessment scores between the simulator group and the traditional group, we found that simulator-based training was more effective in improving trainee scores than traditional training.We attribute this to the advantages of simulators, such as repeatability, zero risk, diverse scenarios, personalized learning, and real-time feedback. Research by Rosen (2017) \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and Grandjean (2021) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003ealso compared the training effectiveness of SBME with traditional training and found that SBME was equally effective as clinical training, with beginners who had weaker foundational knowledge showing more significant learning gains in simulator-based training. We believe that this difference may be due to variations in the baseline levels of trainees in different experiments.\u003c/p\u003e\u003cp\u003eCompetence assessment is typically based on the completion of a required number of clinical cases\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, but minimum case standards vary across regions and organizations\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Additionally, Learning curves differ among trainees and assessment types\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e,meaning that completing the minimum number of cases does not necessarily indicate mastery of skills\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.These differences highlight the importance of the philosophy of Competency-Based Medical Education (CBME),which advocates for the direct observation and assessment of learner' skills\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, due to the unique nature of obstetric ultrasound, implementing assessment often requires the support of volunteer pregnant women, which presents various challenges, including difficulties in large-scale implementation and the complexities of management and coordination. Existing studies indicate that simulators are effective in differentiating the ability levels of trainees in terms of accuracy, speed, and other factors\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.This study found a strong correlation between the performance in simulator assessment and in pregnant woman assessment. This findings is consistent with the studies of Chalouhi (2016)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, Corroenne (2023)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and ATHIEL (2025)\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. It further confirms that simulators can serve as a reliable alternative to real-life assessments of pregnant women, offering a trusted tool for evaluating trainee competency.\u003c/p\u003e\u003cp\u003eThe results of this study also found that simulator test scores were significantly higher than those of the obstetric test, in agreement with the findings of Chalouhi (2016)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Therefore, we suggest that when applying simulator assessments, more stringent minimum passing standards should be established to effectively bridge the gap between simulator and pregnant woman assessments, ensuring that trainees possess the practical operational skills required in clinical settings.\u003c/p\u003e\u003cp\u003eWe acknowledge certain limitations in this study. Firstly, the sample size is relatively small, and some analyses may not be sufficiently robust. However, statistically significant effects were observed in the results, providing a foundation for further large-scale research. If these findings can be replicated in clinical settings, they would have significant clinical implications. Although the methods and training processes employed in this study are highly replicable and can be easily implemented in other institutions, the data collection was limited to a single center, which somewhat restricts the generalizability of the results. But we believe there are no significant differences between the participants in this study and those from other institutions. Furthermore, this study demonstrated the short-term effectiveness of SBME in enhancing learners' skills, but there is a lack of long-term follow-up data to assess its impact on the sustainability of these skills.\u003c/p\u003e\u003cp\u003eWhile SBME can\u0026rsquo;t replace traditional training methods, it can serve as an effective supplement for learner training and assessment. Additionally, further research is needed to explore the potential of virtual reality simulations, in order to better understand their role in skill development and innovation in educational models.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis prospective controlled study evaluates the effectiveness of SBME in obstetric ultrasound skill training and highlights its significant advantages in enhancing clinical competencies among trainees. The results demonstrate that SBME not only improves performance within simulated environments but also facilitates the effective transfer of knowledge and skills from simulation to real clinical practice. Simulation can serve as a powerful complement to traditional training and an effective alternative to assessment methods based on pregnant woman. SBME offers considerable practical value and holds promise for playing an increasingly crucial role in the future development of educational models.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY. Zhao designed the study and organized the research project. J. Zhou analyzed the data and wrote the manuscript. P. Zhou. Dr. Xiaohong Tang revised the study design and the manuscript. S. Guo and L. Li trained and rated the participants. W. Liu and J. Wang collected assessment videos and data. All authors approved the submission of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProject of the 14th five year plan of Educational Science in Hunan Province (XJK21AGD002).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board of The Third Xiangya Hospital of Central South University. (No: 2020-S375). All participants signed informed consent.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha, China; \u003csup\u003e2\u003c/sup\u003eClinical skill center, The Third Xiangya Hospital of Central South University, Changsha, China; \u003cstrong\u003e\u003csup\u003e3\u003c/sup\u003eDe\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003eartment of Ultrasound, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSalomon LJ, Alfirevic Z, Berghella V, et al. 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Program evaluation models and related theories: AMEE Guide No. 67. \u003cem\u003eMedical Teacher\u003c/em\u003e. 2012;34(5):e288-e299. doi:10.3109/0142159x.2012.668637\u003c/li\u003e\n\u003cli\u003eHani S, Chalouhi G, Lakissian Z, Sharara-Chami R. Introduction of Ultrasound Simulation in Medical Education: Exploratory Study. \u003cem\u003eJMIR Medical Education\u003c/em\u003e. 2019;5(2)doi:10.2196/13568\u003c/li\u003e\n\u003cli\u003eRosen H, Windrim R, Lee YM, Gotha L, Perelman V, Ronzoni S. Simulator Based Obstetric Ultrasound Training: A Prospective, Randomized Single-Blinded Study. \u003cem\u003eJournal of Obstetrics and Gynaecology Canada\u003c/em\u003e. 2017;39(3):166-173. doi:10.1016/j.jogc.2016.10.009\u003c/li\u003e\n\u003cli\u003eAndreasen LA, Tabor A, N\u0026oslash;rgaard LN, et al. Multicenter randomized trial exploring effects of simulation‐based ultrasound training on obstetricians\u0026apos; diagnostic accuracy: value for experienced operators. \u003cem\u003eUltrasound in Obstetrics \u0026amp; Gynecology\u003c/em\u003e. 2020;55(4):523-529. doi:10.1002/uog.20362\u003c/li\u003e\n\u003cli\u003eGrandjean GA, Bertholdt C, Zuily S, et al. Fetal biometry in ultrasound: A new approach to assess the long-term impact of simulation on learning patterns. \u003cem\u003eJournal of Gynecology Obstetrics and Human Reproduction\u003c/em\u003e. 2021;50(8)doi:10.1016/j.jogoh.2021.102135\u003c/li\u003e\n\u003cli\u003eLeonardi M, Murji A, D\u0026apos;Souza R. Ultrasound curricula in obstetrics and gynecology training programs. \u003cem\u003eUltrasound in Obstetrics \u0026amp; Gynecology\u003c/em\u003e. 2018;52(2):147-150. doi:10.1002/uog.18978\u003c/li\u003e\n\u003cli\u003eHertzberg BS, Kliewer MA, Bowie JD, et al. Physician training requirements in sonography: how many cases are needed for competence? \u003cem\u003eAJR Am J Roentgenol\u003c/em\u003e. May 2000;174(5):1221-7. doi:10.2214/ajr.174.5.1741221\u003c/li\u003e\n\u003cli\u003eAlrahmani L, Codsi E, Borowski KS. The Current State of Ultrasound Training in Obstetrics and Gynecology Residency Programs. \u003cem\u003eJournal of Ultrasound in Medicine\u003c/em\u003e. 2018;37(9):2201-2207. doi:10.1002/jum.14570\u003c/li\u003e\n\u003cli\u003eSalvesen K\u0026Aring;, Lees C, Tutschek B. Basic European ultrasound training in obstetrics and gynecology: where are we and where do we go from here? \u003cem\u003eUltrasound in Obstetrics \u0026amp; Gynecology\u003c/em\u003e. 2010;36(5):525-529. doi:10.1002/uog.8851\u003c/li\u003e\n\u003cli\u003eLee W, Hodges AN, Williams S, Vettraino IM, McNie B. Fetal Ultrasound Training for Obstetrics and Gynecology Residents. \u003cem\u003eObstetrics \u0026amp; Gynecology\u003c/em\u003e. 2004;103(2):333-338. doi:10.1097/01.AOG.0000109522.51314.5c\u003c/li\u003e\n\u003cli\u003eGabor P, Dimassi K, Aabakke AJM, Rouveau R, Ami O. Ultrasound training in obstetrics and gynecology in Europe: satisfaction survey. \u003cem\u003eUltrasound in Obstetrics \u0026amp; Gynecology\u003c/em\u003e. 2018;51(4):559-560. doi:10.1002/uog.17560\u003c/li\u003e\n\u003cli\u003eTolsgaard MG, Rasmussen MB, Tappert C, et al. Which factors are associated with trainees\u0026apos; confidence in performing obstetric and gynecological ultrasound examinations? \u003cem\u003eUltrasound in Obstetrics \u0026amp; Gynecology\u003c/em\u003e. 2014;43(4):444-451. doi:10.1002/uog.13211\u003c/li\u003e\n\u003cli\u003eBazot M, Dara\u0026iuml; E, Biau DJ, Ballester M, Dessolle L. Learning curve of transvaginal ultrasound for the diagnosis of endometriomas assessed by the cumulative summation test (LC-CUSUM). \u003cem\u003eFertility and Sterility\u003c/em\u003e. 2011;95(1):301-303. doi:10.1016/j.fertnstert.2010.08.033\u003c/li\u003e\n\u003cli\u003ePusic MV, Boutis K, Hatala R, Cook DA. Learning Curves in Health Professions Education. \u003cem\u003eAcademic Medicine\u003c/em\u003e. 2015;90(8):1034-1042. doi:10.1097/acm.0000000000000681\u003c/li\u003e\n\u003cli\u003eTolsgaard MG, Chalouhi GE. Use of ultrasound simulators for assessment of trainee competence: trendy toys or valuable instruments? \u003cem\u003eUltrasound in Obstetrics \u0026amp; Gynecology\u003c/em\u003e. 2018;52(4):424-426. doi:10.1002/uog.19071\u003c/li\u003e\n\u003cli\u003eHolmboe ES, Sherbino J, Long DM, Swing SR, Frank JR. The role of assessment in competency-based medical education. \u003cem\u003eMed Teach\u003c/em\u003e. 2010;32(8):676-82. doi:10.3109/0142159x.2010.500704\u003c/li\u003e\n\u003cli\u003eMadsen ME, Konge L, N\u0026oslash;rgaard LN, et al. Assessment of performance measures and learning curves for use of a virtual-reality ultrasound simulator in transvaginal ultrasound examination. \u003cem\u003eUltrasound in Obstetrics \u0026amp; Gynecology\u003c/em\u003e. 2014;44(6):693-699. doi:10.1002/uog.13400\u003c/li\u003e\n\u003cli\u003eDyre L, N\u0026oslash;rgaard L, Tabor A, et al. Collecting Validity Evidence for the Assessment of Mastery Learning in Simulation-Based Ultrasound Training. \u003cem\u003eUltraschall in der Medizin - European Journal of Ultrasound\u003c/em\u003e. 2016;37(04):386-392. doi:10.1055/s-0041-107976\u003c/li\u003e\n\u003cli\u003eChalouhi GE, Bernardi V, Gueneuc A, Houssin I, Stirnemann JJ, Ville Y. Evaluation of trainees\u0026rsquo; ability to perform obstetrical ultrasound using simulation: challenges and opportunities. \u003cem\u003eAmerican Journal of Obstetrics and Gynecology\u003c/em\u003e. 2016;214(4):525.e1-525.e8. doi:10.1016/j.ajog.2015.10.932\u003c/li\u003e\n\u003cli\u003eCorroenne R, Jacquier M, Stirnemann J, Salomon LJ, Ville Y, Chalouhi G. Evaluation of optical positioning ultrasound simulator for assessment of trainee ability in obstetric ultrasound. \u003cem\u003eUltrasound in Obstetrics \u0026amp; Gynecology\u003c/em\u003e. 2023;63(1):115-116. doi:10.1002/uog.26295\u003c/li\u003e\n\u003cli\u003eAthiel Y, Defrance M, Noble P, et al. Comparative evaluation of two simulation technologies for obstetric ultrasound trainees\u0026rsquo; assessment. \u003cem\u003eEuropean Journal of Obstetrics \u0026amp; Gynecology and Reproductive Biology\u003c/em\u003e. 2025;306:81-86. doi:10.1016/j.ejogrb.2025.01.014\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ultrasound, Obstetric, simulation, simulation based medical education","lastPublishedDoi":"10.21203/rs.3.rs-7132399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7132399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTraditional training in obstetric ultrasound is constrained by patient availability and ethical concerns. In contrast, simulation-based medical education offers a safe, repeatable, and realistic alternative by replicating clinical environments for hands-on practice. This study aims to evaluate the clinical transferability of skills acquired through simulation and compare its effectiveness with traditional training in improving residents\u0026rsquo; ultrasound competencies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis prospective randomized controlled study included 22 residents, randomly assigned to either the simulator (n\u0026thinsp;=\u0026thinsp;11) or traditional (n\u0026thinsp;=\u0026thinsp;11) group. All participants underwent short-term mid-trimester fetal ultrasound training, using either a simulator or real pregnant women. Participants were required to perform assessments on both the simulator and volunteer pregnant women before and after training. Assessment scores were evaluated by ultrasound consultants using Objective Ultrasound Competency Assessment Tool.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAfter training, both groups showed significant improvement in post-test scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Trainees in the simulator group not only demonstrated enhanced assessment scores in the simulated environment(57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0vs81.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but also exhibited notable improvements in their skills in the real clinical setting(53.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8vs74.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). A comparison of the assessment scores between the two groups revealed that the type of training had a significant impact on the residents' performance improvement, with simulator-based training proving to be more effective than traditional training(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and showing a large effect size(Partial η\u0026sup2;\u0026gt;0.14). Pearson correlation analysis indicated a strong correlation between the simulator assessment scores and the pregnant women assessment scores (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eSimulation-Based Medical Education enhances residents' performance in both simulated environments and real clinical settings and is more effective than traditional clinical training methods. Additionally, simulation can serve as a powerful complement to traditional training and a safe alternative to patient-based assessment methods involving pregnant women.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003eThis trial was registered at the Chinese Clinical Trial Registry with registration number ChiCTR2500107408 on August 11, 2025 (Retrospectively registered).\u003c/p\u003e","manuscriptTitle":"Evaluating the Effectiveness of High-Fidelity Simulation-Based Training on Clinical Skill Transfer in Obstetric Ultrasound Residents: A Prospective Randomized Controlled Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 10:23:51","doi":"10.21203/rs.3.rs-7132399/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-28T06:19:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-27T19:19:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15009744427829772454480356194721385189","date":"2025-11-11T12:54:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T14:12:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77689972180348120530665701380733447479","date":"2025-11-02T14:07:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-13T07:43:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T07:39:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-12T06:15:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-11T15:59:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-08-11T15:56:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec0cc3be-e64d-4ebb-b4f0-e0b631da195b","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:07:55+00:00","versionOfRecord":{"articleIdentity":"rs-7132399","link":"https://doi.org/10.1186/s12909-026-08835-2","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2026-02-17 15:59:34","publishedOnDateReadable":"February 17th, 2026"},"versionCreatedAt":"2025-08-21 10:23:51","video":"","vorDoi":"10.1186/s12909-026-08835-2","vorDoiUrl":"https://doi.org/10.1186/s12909-026-08835-2","workflowStages":[]},"version":"v1","identity":"rs-7132399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7132399","identity":"rs-7132399","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}