2 research outputs found
Closing the Feedback Loop: Collaborative Design of a Musculoskeletal Revision Course
Clinical Teaching Fellows (CTFs) at Leicester Medical School decided to supplement the learning needs of first year students on the musculoskeletal (MSK) module. Mid-unit evaluation had demonstrated that students had remarked positively on CTF teaching and many requested further anatomy teaching and demonstrations using prosections. It is increasingly important to provide students with evidence that their feedback is being acted upon, therefore CTFs collaborated in providing additional learning resources in the form of CTF-led revision courses while the MSK module was still ongoing. A survey was designed which aimed to engage students and to further explore their learning needs when developing the course. Based on these responses, two half-day CTF-led revision courses were designed, which included educational methods and topics the students themselves had suggested. CTFs collaboratively developed eight different stations, with one CTF designing and delivering the teaching material. Attendance was high and feedback indicated this was a valuable learning experience for students, with particularly positive responses about the interactive nature and high quality of the teaching. This experience demonstrates the benefits of working in partnership with students when developing learning activities, closing the feedback loop to improve student satisfaction, and collaborative planning when designing revision resources
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers