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Abstract No pan‐India‐specific guidelines exist for the management of urological cancers. Although western guidelines are useful for informing management strategies, they do not account for the nuances of management in the Indian context. A modified Delphi method was used to provide a framework for the systematic development of India‐centric guidelines for the management of three uro‐oncology disease states: small renal masses, non‐muscle invasive bladder cancer and high‐risk/locally advanced prostate cancer.

Abstract Background When faced with treatment options, patients are asked to participate in decision‐making. We sought to determine which treatment aspects matter most for individuals treated for muscle invasive bladder cancer (MIBC), with an aim to improve understanding of patient preferences and what trade‐offs patients are willing to accept. Our study consisted of a discrete choice experiment (DCE): a type of questionnaire used to elicit preferences in the absence of real‐world choice. Methods The DCE had five attributives, each with three levels. Participants were asked to complete a questionnaire in which they were asked to choose between two hypothetical MIBC treatments. The data were analysed using a conditional logit model, and preferences for, and trade‐offs between, attributes were estimated. Results We recruited patients with MIBC who had either already completed, were undergoing or had yet to commence radical treatment for MIBC (n = 60). Participants indicated a strong preference for treatments that increased their life expectancy (p =

PurposeThe Graham Roberts Study was initiated in 2018 and is the first Trials Within Cohorts (TwiCs) study for bladder cancer. Its purpose is to provide an infrastructure for answering a breadth of research questions, including clinical, mechanistic, and supportive care centred questions for bladder cancer patients.ParticipantsAll consented patients are those aged 18 or older, able to provide signed informedconsent and have a diagnosis of new or recurrent bladder cancer. All patients are required to have completed a series of baseline questionnaires. The questionnaires are then sent out every 12 months and include information on demographics and medical history as well as questionnaires to collect information on quality of life, fatigue, depression, overall health, physical activity, and dietary habits. Clinical information such as tumor stage, grade and treatment has also been extracted for each patient.Findings to dateTo date, a total of 125 bladder cancer patients have been consented onto the study with 106 filling in the baseline questionnaire. The cohort is made up of 75% newly diagnosed bladder cancer patients and 66% non-muscle invasive bladder cancer cases. At present, there is 1-year follow-up information for 70 patients, 2-year follow-up for 57 patients, 3-year follow-up for 47 patients and 4-year follow-up for 19 patients.Future plansWe plan to continue recruiting further patients into the cohort study. Using the data collected within the study, we hope to carry out independent research studies with a focus on quality of life. We are also committed to utilizing the Roberts Study Cohort to set up and commence an intervention. The future studies and trials carried out using the Roberts Cohort have the potential to identify and develop interventions that could improve the prevention, diagnosis, and treatment of bladder cancer.

To succeed in the information age, students need to learn to communicate their understanding of complex topics effectively. This is reflected in both educational standards and standardized tests. To improve their writing ability for highly structured domains like scientific explanations, students need feedback that accurately reflects the "structure" of their explanations -- how they express causal relationships between concepts, and link them together. Unfortunately, providing that type of feedback is difficult and time-consuming for teachers. And Automated Essay Scoring (AES) systems generally aggregate the occurrence of features to provide holistic scores for essays, but give no information about the causal relationships identified by the students. In this paper, we evaluate a method for identifying causal relations in student scientific explanations based on recent advances in Natural Language Processing (NLP). Identification of the causal structure could be used to provide formative feedback to writers and their teachers about how the writing and reasoning could be improved. The identification method we evaluate here is based on Bidirectional Encoder Representations from Transformers (BERT, Devlin et al., 2019), which uses unsupervised learning to create a general language model and then supervised learning to apply that model to a particular task. We compare a base BERT model to several of its special-purpose variants, and include two baseline approaches. The contributions of this study were that we determine which variants worked best along with their strengths and limitations for identifying causal structure in explanations. We found that the models detected the existence of a causal relationship with good performance, but identifying the particular causal relationship will require further research, largely due to the common challenge of imbalanced data classes.