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Background The CheckMate 025 trial established nivolumab monotherapy as one of the standards of care in previously treated advanced or metastatic renal cell carcinoma (aRCC). However, supporting real-world data is lacking. This study investigated characteristics, treatment sequences and clinical outcomes of patients who received nivolumab monotherapy for previously treated aRCC in the UK. Methods This was a retrospective cohort study of aRCC patients treated with nivolumab at second line or later (2L +) at 4 UK oncology centres. Eligible patients commenced nivolumab (index date) between 01 March 2016 and 30 June 2018 (index period). Study data were extracted from medical records using an electronic case report form. Data cut-off (end of follow-up) was 31 May 2019. Results In total, 151 patients were included with median follow-up of 15.2 months. Mean age was 66.9 years, male preponderance (72.2%), and mostly Eastern Cooperative Oncology Group performance status grade 0–1 (71.5%). Amongst 112 patients with a known International Metastatic RCC Database Consortium score, distribution between favourable, intermediate, and poor risk categories was 20.5%, 53.6%, and 25.9% respectively. The majority of patients ( n  = 109; 72.2%) received nivolumab at 2L, and these patients had a median overall survival (OS) of 23.0 months [95% confidence interval: 17.2, not reached]. All patients who received nivolumab at 2L had received TKIs at 1L. Amongst the 42 patients (27.8%) who received nivolumab in third line or later (3L +) the median OS was 12.4 months [95% CI: 8.8, 23.2]. The most common reasons for nivolumab discontinuation were disease progression (2L: 61.2%; 3L: 68.8%) and adverse events (2L: 34.7%; 3L: 28.1%). Conclusion This study provides real-world evidence on the characteristics, treatment sequences, and outcomes of aRCC patients who received 2L + nivolumab monotherapy in the UK. Nivolumab-specific survival outcomes were similar to those achieved in the CheckMate 025 trial.

Cutting strategy research has traditionally been focused on hard materials that are intrinsically difficult to machine. An increase in the desire for personalized products has led to the requirement of the direct machining of polymers for personalized products. Little research is evident in the literature on the analysis of optimal machining parameters for machining materials such as polypropylene. One of the vital factors that affects the quality of polypropylene products and the respective machining strategy is surface roughness. This research is aimed at extracting information on the machining of polypropylene materials. A surface roughness predictive model based on neural networks has been developed. The design of experiments approach is used to obtain an adequate predictive model for the process planning which is further utilized as an input to the predictive model. The model mainly hinges on three independent variables namely spindle speed, feed rate, and depth of cut. Extensive experimental work on different network topologies and training algorithms has been performed to predict the behaviour of the surface roughness for machined polypropylene products. The results illustrate the benefits of being able to determine surface roughness values. This allows for the determination of optimal cutting strategies and tooling for the required surface roughness. The performance predictive model has been found to be satisfactory over the dataset for polypropylene machining. Hypothesis testing has also been carried out to identify the confidence of the predictive model.

Orthopaedic registrars working in non-trauma centres participate in non-resident on-call rotas. These rely on a dedicated junior resident tier to provide 'first-on' cover. This responsibility is usually shared among core surgical trainees although it is increasingly being taken on by foundation year two and general practice vocational training scheme doctors with no previous experience of orthopaedics or Advanced Trauma Life Support ® training.