Background Heterogeneous access to clinical learning opportunities and inconsistency in teaching is a common source of dissatisfaction among medical students. This was exacerbated during the COVID‐19 pandemic, with limited exposure to patients for clinical teaching. Methods We conducted a proof‐of‐concept study at a London teaching hospital using mixed reality (MR) technology (HoloLens2™) to deliver a remote access teaching ward round. Results Students unanimously agreed that use of this technology was enjoyable and provided teaching that was otherwise inaccessible. The majority of participants gave positive feedback on the MR (holographic) content used (n = 8 out of 11) and agreed they could interact with and have their questions answered by the clinician leading the ward round (n = 9). Quantitative and free text feedback from students, patients and faculty members demonstrated that this is a feasible, acceptable and effective method for delivery of clinical education. Discussion We have used this technology in a novel way to transform the delivery of medical education and enable consistent access to high‐quality teaching. This can now be integrated across the curriculum and will include remote access to specialist clinics and surgery. A library of bespoke MR educational resources will be created for future generations of medical students and doctors to use on an international scale.

Background The University Clinical Aptitude Test (UCAT) is an admissions assessment used by a consortium of universities across the UK, Australia, and New Zealand, to aid the selection of applicants to medical and dental degree programmes. The UCAT aims to measure the mental aptitude and professional behaviours required to become successful doctors and dentists. We conducted a systematic review to establish the predictive value of the UCAT across measures of performance at undergraduate and post-graduate levels. Methods A literature search was conducted in April 2020 using eight electronic databases: MEDLINE, APA PsycInfo, SCOPUS, Web of Science, EThOS, OpenGrey, PROSPERO, and the UCAT website. Data were extracted from selected studies and tabulated as results matrices. A narrative synthesis was performed. Results Twenty-four studies satisfied our inclusion criteria, 23 of which were deemed to be of good quality (using the Newcastle-Ottawa Scale). For over 70% of univariate data points, the UCAT exerted no statistically significant predictive validity; for the remainder, predictive power was weak. The cognitive total and verbal reasoning subtests had the largest evidence base as weakly positive predictors of academic performance. The SJT subtest was a weak predictor of professional behaviour during medical school. Studies specific to dental schools demonstrated variable findings across the five studies. Only 1 study looked at post-graduate outcome measures and demonstrated that the UCAT was not a predictor of health- or conduct-related fitness to practice declarations at GMC registration. Conclusions These data provide some support for the use of cognitive total and verbal reasoning subtests as part of medical school selection. Further research is needed to investigate outcomes beyond professional registration and for dental students.

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Change controlVersion 5 1 Version 5 of this document contains changes to:1.1 Who can apply for a dataset (paragraphs 15 and 16). Information for student applicants 1.2 Stage 7 -Software arrangements and configuring the Safe Haven (paragraphs 44 -48). To improve data management data extracts will now be provided to researchers as a database in the Safe Haven. GMC staff will be able to assist researchers with connecting to analysis packages to their research extract database. 1.3Stage 8 -Review of analysis outputs against Statistical Disclosure Controls (paragraph 58). Statistical disclosure controls for charts and visualisations 2 Version 4 of this document reflects the following changes: 2.1 New datasets (BioMedical Admissions Test -[BMAT] scores and UCAS applications); 2.2 The option of bringing in additional datasets -paragraph 9 ; 2.3 Clarification of exemption from ethics approval -paragraph 14; 2.4 Archiving data extracts; 2.5 Note that there is a separate process for access to extracts for medical school entry profiles and workforce planning. This may be more suitable for those 2 of 50 working for an applicable organisation who do not wish to publish research. Please see UKMED training pathway analysis. * 3 Appendix B -GMC template Data Sharing Agreement has been updated to reflect the commencement of the General Data Protection Regulation (GDPR) on 25 May 2018. 4 Appendix C -Data User Agreement to access the HIC Safe Haven for approved UKMED Research Projects has been updated to reflect the commencement of the General Data Protection Regulation (GDPR) on 25 May 2018. Version 3 5 Amendments have been made to paragraphs 22, 23, 54 and 55 to ensure the involvement of colleges in requests involving exam data.

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