BACKGROUND: Previous reports have shown that there are long waiting times to commence therapy in the community-based mental health programme, IAPT (Improving Access to Psychological Therapies). OBJECTIVE: This study aimed to explore both causes and potential solutions to alleviate the burden of these waits. METHODS: A Systematic Literature Review (SLR) and Semi-Structured Interviews (SSIs) were conducted to identify causes and effects of these waits. Consequently, meaningful recommendations were made and tested with the aim of improving IAPT’s waiting times. RESULTS: SLR and SSIs revealed high ‘Did Not Attend’ (DNA) rates and a lack of support between initial appointments as being both a cause and effect of long waits. The identified issues were tackled with the development of an App design. Expert interviews and a mass survey fuelled the iterative process leading to a final prototype. Notable features included: therapist profile page, smart appointment reminders and patient timeline. Positive feedback was received from university students and ICS Digital, with scope to trial the App within Manchester CCG. CONCLUSIONS: In the long run, the App aims to indirectly shorten waiting times by addressing treatment expectations and serving as an IAPT companion along the patient journey, thus reducing anxiety and consequently DNAs.

To evaluate the status of UK undergraduate urology teaching against the British Association of Urological Surgeons (BAUS) Undergraduate Syllabus for Urology. Secondary objectives included evaluating the type and quantity of teaching provided, the reported performance rate of General Medical Council (GMC)-mandated urological procedures, and the proportion of undergraduates considering urology as a career. Subjects and MethodsThe uroLogical tEAching in bRitish medical schools Nationally (LEARN) study was a national multicentre cross-sectional evaluation. Year 2 to Year 5 medical students and Foundation Year (FY) 1 doctors were invited to complete a survey between 3 October and 20 December 2020, retrospectively assessing the urology teaching received to date. Results are reported according to the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). ResultsIn all, 7063/8346 (84.6%) responses from all 39 UK medical schools were included; 1127/7063 (16.0%) were from FY1 doctors who reported that the most frequently taught topics in undergraduate training were on urinary tract infection (96.5%), acute kidney injury (95.9%) and haematuria (94.4%). The most infrequently taught topics were male urinary incontinence (59.4%), male infertility (52.4%) and erectile dysfunction (43.8%). Male and female catheterisation on patients as undergraduates was performed by 92.1% and 73.0% of FY1 doctors respectively, and 16.9% had considered a career in urology. Theory-based teaching was mainly prevalent in the early years of medical school, with clinical skills teaching, and

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INTRODUCTION AND OBJECTIVE: Indications for management of ureteral stones are unclear, and clinician determines whether to wait for spontaneous ureteral stone passage (SSP) or perform active treatment, especially in well-controlled patients to avoid unwanted complications. Therefore, suggesting the possibility of SSP would help to make a clinical decision regarding ureteral stones. Therefore, we aimed to develop a prediction model of SSP using machine learning and logistic regression and to compare the performance of the two models.METHODS: Patients diagnosed with unilateral ureteral stones at our emergency department between August 2014 and September 2018 were included and underwent non-contrast-enhanced computed tomography at 4 weeks from the first stone episode. Predictors of SSP were applied to build and validate the prediction model using multilayer perceptron (MLP) with the Keras framework.RESULTS: Of 833 patients, SSP was observed in 606 (72.7%). SSP rates were 68.2% and 75.6%, for stone sizes 5e10 mm and <5 mm, respectively. Stone opacity, location, and whether it was the first ureteral stone episode were significant predictors of SSP. AUCs for ROC curves for MLP and logistic regression were 0.859 and 0.847 for stones <5 mm and 0.881 and 0.817 for those 5e10 mm, respectively.CONCLUSIONS: Prediction models of SSP were developed in patients with well-controlled unilateral ureteral stone; the performance of the models was good, especially in identifying SSP for 5e10-mm ureter stones without definite treatment guidelines.To further improve the performance of these models, future studies should focus on using machine learning techniques in image analysis.