Journal of surgical education最新文献

Objectives: To identify what types of models exist for peer teaching in surgery to acquire surgical skills, to summarize the impact of peer teaching models and to discuss their potential implications on competency-based medical education.

Design: In this systematic review, MEDLINE, EMBASE, and Web of Science were searched from inception to May 14, 2025. Randomized controlled trials (RCTs), cohort, cross-sectional, and quasi-experimental designs evaluating peer or near-peer teaching for surgical skill acquisition were included. Risk of bias was assessed using Cochrane RoB 2.0, ROBINS-I, and JBI checklists.

Setting: Studies were conducted in undergraduate and postgraduate surgical training programs across academic institutions internationally.

Participants: Twenty-one studies (12 RCTs, 1 cross-sectional, 1 prospective cohort, 6 pre-post) were included. Participants included medical students and residents serving as learners or peer-teachers.

Results: Peer and near-peer instruction improved learner confidence and technical proficiency, with performance outcomes often similar to those observed in faculty-led instruction for basic surgical skills. Workshop (n = 8), classroom (n = 8), and simulation (n = 4) models most commonly targeted suturing, knot-tying, and laparoscopic skills. Effectiveness was greatest when peer-teacher experience matched skill complexity. Resident-led models effectively taught advanced laparoscopic and robotic techniques, whereas medical student tutors were most effective for foundational tasks. Most studies (81%) demonstrated moderate risk of bias, with only 2 assessing teacher outcomes.

Conclusion: Peer and near-peer teaching provide an effective approach for surgical skill development, supporting competency-based medical education. Structured alignment of skill complexity with peer-teacher expertise may achieve competency outcomes comparable to faculty instruction while also enhancing learner confidence, teaching competence, and scalability in surgical education.

Objective: This study aimed to identify the scope of VCE responsibilities as well as areas of improvement for the VCE role.

Design: An 11-item free response/multiple-choice survey was distributed to prospective participants. Quantitative data was analyzed using descriptive statistics (mean and standard deviation) and qualitative methods were used to analyze free response data.

Setting: Participants completed the survey electronically.

Participants: Current vice chairs of education from across the United States were recruited.

Results: Twenty-five of 60 identified VCE (42% response rate) completed the survey. Responders held the VCE position for 4.7±3.2 years. The majority of respondents reported that they oversaw all educational activities in their department. Twenty-two respondents (88%) indicated that their job responsibilities were appropriate, while two (8%) felt their roles were not well-defined. Four VCE (16%) desired more control over the departmental budget for education-related activities, while two (8%) felt that their role was undermined by their Department Chairperson. Oversight and coordination of departmental education activities was the most frequently reported value for the VCE role. Eighteen respondents (72%) received compensation for their VCE role, but 7 (28%) did not.

Conclusions: The results of this survey provide insight into the responsibilities and perceived value of the VCE role in surgery. This survey also identified areas of concern which merit intradepartmental examination in order to improve the effectiveness of the VCE role at a local level.

Generative artificial intelligence (GAI) is anticipated to transform medical education, and many studies have already been published reporting its effectiveness, accuracy, and feasibility as a "tutor" to help medical students learn in different didactic contexts. This perspective reviews this literature and highlights an understudied use-case for GAI: helping medical students to prepare for context-specific intraoperative learning during surgical clerkships. We propose a "librarian," rather than tutor, method for GAI deployment in undergraduate medical education, where students dictate their learning objectives and receive specific direction to guide their individualized preparation, instead of the passive reception of didactic information which defines tutor-GAI use. We describe the librarian-GAI model in practical terms, providing template queries and prompt-engineering guidance for learners.

Background: Applications to integrated plastic surgery residency programs have outpaced the growth of available positions. As a result, faculty must review more applications each year. Artificial intelligence provides 1 mechanism for holistic, expedited review of applications. We aimed to develop and validate a machine learning (ML)-based tool to screen residency applications and hypothetically identify which candidates would receive interview invitations.

Methods: We retrospectively collected all applications submitted to 1 integrated plastic surgery residency program between 2022 and 2025. We extracted application data and processed it through 4 ML models: XGBoost, Random Forest, CatBoost, and LightGBM. We trained and validated these models on data from the 2022-2024 application cycles and tested them on applications from 2025. The success of the model was judged against the interview decisions of faculty in the 2025 cycle. Measures of model performance included area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and F1 score. Shapley additive explanations analysis determined the relative contribution of each variable to the model outcome.

Results: The top performing model was the CatBoost algorithm. The AUROC was 0.92, and the AUPRC was 0.668, indicating excellent performance. With a threshold to maximize F1 score, sensitivity was 95% and specificity was 67%. Total number of publications was the most important feature influencing the interview invitation decision.

Conclusions: We developed and validated a ML algorithm to accurately aid in the selection of residency interviewees. This tool may assist faculty in conducting more holistic and efficient application review.

Introduction: Surgical trainees are increasingly using large language models (LLMs) for clinical and surgical preparation. However, there has been less discussion about the specific ways learners are utilizing artificial intelligence in pursuit of surgical competence. Adding challenge, the rapid pace of advancement has made it difficult for educators to remain aware of the different systems available and the best practices for their utilization.

Methods: Using relevant surgical preparation scenarios, we discuss several custom learning tools that can be created by LLMs today. We then demonstrate three principles for success using specific examples: selecting the "best" LLM, having clear learning goals, and providing sufficient context. Finally, we discuss current limitations of working with LLMs including hallucinations, bias, and sycophancy.

Conclusions: Effective LLM utilization transforms the interaction from simple informational exchange to an active learning experience. By providing clear examples and approaches, we hope to both make these systems more approachable for educators, as well as inspire learners to create their own custom tools.

Objective: To improve early-career doctor preparedness for recognizing factors influencing decision-making in surgical patients, using novel simulation.

Design: A modified video-based simulation technique (Shadowbox) was used to develop decision-making skills. Case vignettes reflecting real-life scenarios were developed, with additional complexities (e.g., anticoagulation therapy) included. After working through cases and formulating a management plan, participants were shown videos of how senior surgical residents responded, unscripted, to each scenario. The facilitated debrief allowed for participant reflection on the differences between management plans and exploration of how the surgical resident weighted decision-making factors. Participants mean pre- and post self-reported preparedness to identify factors influencing decision-making in surgical patients were compared using a 7-point Likert scale, and free-text comments were used to provide further insights.

Setting: Three hospitals delivering acute surgical services, within a single Scottish health board.

Participants: Postgraduate year 2 doctors not in a formal surgical training program.

Results: Over 3 sessions, 57 participants attended. The majority felt the simulation helped develop decision-making skills (n = 43, 75.4 % rating 6 or 7) and that the cases were "engaging." Identification of patient (4.9 vs. 5.6, p < 0.01), surgical (3.9 vs. 5.3, p < 0.01), radiological (3.4 vs. 4.8, p < 0.01), and social factors (5.1 vs. 5.8, p = 0.02) influencing decision-making all improved. Recognition of when to consider a change from conservative to operative management also improved (4.2 vs. 5.5, p = 0.01).

Conclusions: A modified Shadowbox technique is an effective and scalable method of enhancing surgical decision-making training. Through adapting cases, a similar approach could be used for higher surgical residents, across a range of surgical specialties.

Background: Trauma and Orthopedic national selection is a highly competitive process with a competition ratio of 3.02:1 in 2023. Historically London and Thames Valley deaneries have the highest success rates with anecdotally over 75% appointed at the first attempt. Our aim was to improve success rates for trainees in Yorkshire and Humber by implementing a free series of mock interviews and workshops.

Methods: Trainees recently appointed through the ST3 interviews, and a good understanding of the national selection process assembled a local faculty. 15 local delegates were identified. There were 3 teaching workshop sessions covering the 4 different interview stations. A total of 32 mock interviews were run, with space for 2 candidates in each interview., 64 interview opportunities meant an average 4 mocks per person. Time was designated within the mocks to give direct feedback to candidates. Data was collected prospectively on each session., Candidates will also asked to self-report their performance in ST3 national selection.

Results: 11/15 (73%) candidates successfully achieved a score highly enough to be offered a training post, 10/11 accepted the training post they were offered., 8/10 were appointed to their first-choice training scheme. Candidates who attended 3 or more mock interviews had an 86% success rate, with 73% (8/11) candidates were successful at the first attempt.

Conclusion/findings: A focused ST3 preparation course with appropriately skilled faculty can significantly increase applicant success. Free preparation courses should be widely available to ensure equity for all those applying. As a profession we have a responsibility to support our trainees to enable them to succeed. Regional support may help create a fair interview process allowing us to select the best candidates to be our future colleagues.

Objective: To assess for differences in obstetric procedural experiences on the obstetrics and gynecology (OB/GYN) clerkship based on medical student race and gender.

Design: IRB-approved retrospective cohort study of medical student case logs on their OB/GYN clerkship between 2014 and 2022.

Setting: Single United States allopathic medical school, with clinical rotations at a tertiary care center-Duke University School of Medicine, Durham, North Carolina PARTICIPANTS: 918 medical students who completed their OB/GYN clerkship between 2014 and 2022. Students with missing race or gender data from the information shared by the Assistant Dean of Education were excluded, resulting in a total of 869 students included in the analysis.

Results: Among 869 students included in the study, 59.0% were female and 23.9% identified as underrepresented minorities in medicine (URM). There were no significant differences in "hands-on" experience in vaginal or cesarean deliveries based on medical student race or gender. However, URM male students participated in 0.74 fewer vaginal deliveries (95% CI: -1.35 to -0.12) and 1 less total delivery (95% CI: -1.91 to -0.07) than non-URM female students, while non-URM male students participated in 0.45 fewer vaginal deliveries (95% CI: -0.87 to -0.03) than non-URM female peers. These results were obtained while controlling for differences based on academic year and clerkship timing within the academic year.

Conclusions: No differences were noted in "hands-on" delivery experience during the OB/GYN clerkship based on medical student gender or URM status, however subgroup analyses revealed that URM and non-URM male students participated in fewer vaginal deliveries compared to non-URM female peers. These differences, although modest, highlight potential disparities in clinical exposure and inclusion in the learning environment, warranting further investigation and underscoring the need for continued efforts to ensure equitable learning opportunities for all students.