Teaching and Assessment of Medical Students During Complex Multifactorial Team-Based Tasks: The "Virtual on Call" Case Study.

Abstract: Although considerable efforts have been made to incorporate simulation-based learning (SBL) in undergraduate medical education, to date, most of the medical school curricula still focus on pure knowledge or individual assessment of objective structured clinical examination skills (OSCE). To this end, we designed a case study named "iG4 (integrated generation 4) virtual on-call (iVOC)". We aimed to simulate an on-call shift in a high-fidelity virtual hospital setting in order to assess delegates' team-based performance on tasks related to patient handovers (prioritisation, team allocation). Methods: A total of 41 clinical year medical students were split into 3 cohorts, each of which included 3 groups of 4 or 5 people. The groups consisted of a structured mix of educational and cultural backgrounds of students to achieve homogeneity. Each performing group received the handover for 5 patients in the virtual hospital and had to identify and deal with the acutely unwell ones within 15 minutes. We used TEAMTM tool to assess team-based performances. Results: The mean handover performance was 5.44/10 ± 2.24 which was the lowest across any performance marker. The overall global performance across any team was 6.64/10 ± 2.11. The first rotating team's global performance for each cycle was 6.44/10 ± 2.01, for the second 7.89/10 ± 2.09 and for the third 6.78/10 ± 1.64 (p = 0.099 between groups). Conclusion: This is one of the first reported, high-fidelity, globally reproducible SBL settings to assess the capacity of students to work as part of a multinational team, highlighting several aspects that need to be addressed during undergraduate studies. Medical schools should consider similar efforts with the aim to incorporate assessment frameworks for individual performances of students as part of a team, which can be a stepping-stone for enhancing safety in clinical practice.

Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward?

An increasing trend in funding towards artificial intelligence (AI) research in medicine has re-animated huge expectations for future applications. Obstetrics and gynaecology remain highly litigious specialities, accounting for a large proportion of indemnity payments due to poor outcomes. Several challenges have to be faced in order to improve current clinical practice in both obstetrics and gynaecology. For instance, a complete understanding of fetal physiology and establishing accurately predictive antepartum and intrapartum monitoring are yet to be achieved. In gynaecology, the complexity of molecular biology results in a lack of understanding of gynaecological cancer, which also contributes to poor outcomes. In this review, we aim to describe some important applications of AI in obstetrics and gynaecology. We also discuss whether AI can lead to a deeper understanding of pathophysiological concepts in obstetrics and gynaecology, allowing delineation of some grey zones, leading to improved healthcare provision. We conclude that AI can be used as a promising tool in obstetrics and gynaecology, as an approach to resolve several longstanding challenges; AI may also be a means to augment knowledge and assist clinicians in decision-making in a variety of areas in obstetrics and gynaecology.

The Role of KRAS in Endometrial Cancer: A Mini-Review.

Endometrial cancer (EC) is the most common cancer of the female genital tract, resulting annually in 76,000 related deaths worldwide. EC originates either from oestrogen-related proliferative endometrium (type I, endometrioid), or from atrophic endometrium (type II, non-endometrioid). Each type of EC is characterized by different molecular profile alterations. The Kirsten rat sarcoma viral oncogene homolog (KRAS) gene encodes a signalling protein which moderates response to various extracellular signals via down-regulation of the mitogen-activated protein kinase (MAPK) or phosphoinositide-3-kinase/v-akt murine thymoma viral oncogene (PI3K/AKT) pathways. This article reviews the role of KRAS in predicting transition from hyperplastic endometrium to early-stage well-differentiated EC, as well as further invasive proliferation of the tumour to advanced-stage disease. KRAS seems to be directly associated with type I EC, and most studies support its early involvement in carcinogenesis. Current evidence correlates KRAS mutations with increased cell proliferation and apoptosis, as well as up-regulation of endometrial cell oestrogen receptors. Tumours positive for KRAS mutation can harbour hypermethylation-related changes in genome expression, and this can be the cause of concurrent loss of DNA repair proteins. Despite some evidence that KRAS mutation status affects cancer progression, a consensus is yet to be reached. Based on the available evidence, we suggest that screening for KRAS mutations in patients with hyperplastic endometrium or early-stage type I EC, may provide important information for prognosis stratification, and further provision of personalised treatment options.