Postgraduate Course: Clinical Project (STEM11010)
Course Outline
School | Deanery of Clinical Sciences |
College | College of Medicine and Veterinary Medicine |
Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Course type | Online Distance Learning |
Availability | Not available to visiting students |
SCQF Credits | 20 |
ECTS Credits | 10 |
Summary | In this course you will have multiple opportunities to explore real clinical data gathered (and anonymised) through the Ann Rowling Regenerative Neurology Clinic. Examples could be a clinical audit or systematically reviewing data collected from hundreds of patients from clinical trials for neurodegenerative diseases. You will get to analyse these datasets and gain feedback prior to assessments. This course will help with your understating of the background to a clinical dataset, its relationship to the literature and how it may be utilised, mined and/or expanded. |
Course description |
This course will allow students to experience and work with real clinical datasets as well as learning how they are generated and the ethical and regulatory rules that govern them.
This course will start by laying the groundwork of clinical data gathering as well as ethical and regulatory rules before letting students decide upon several clinical datasets that they will then need to mine and present their results. The assessments will be designed around specific pillars of the datasets and for example may include presenting the background and need for the dataset, followed by their results and finally a conclusion on what further data needs to be collected to improve and widen their results.
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Entry Requirements (not applicable to Visiting Students)
Pre-requisites |
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Co-requisites | |
Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of clinical datasets and how to analyse them
- Evaluate, criticise and appraise the literature around this topic
- Demonstrate their advancement in the basic research skills vital for clinical data understanding and displayed evidence-based practice and the application of theory to clinical data sets
- Communicate and engage with the course¿s concepts and principles with others outwith their own field
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Additional Information
Graduate Attributes and Skills |
See learning outcomes below as well but specifically:
1. Due to working independently students must show a high degree of autonomy and time management skills to complete this course
2. Students will need to display a significant individual drive and determination to engage with this course and remain focused
3. Students will need to not only critically assess their own data with respect to drawing conclusions but this will also allow students to gain an understanding of real-life clinical gathering and how to mine that data
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Keywords | Regeneration,Neurodegenerative diseases,translation,neurology,clinical trials,data handling |
Contacts
Course organiser | Dr David Hampton
Tel: (0131) 242 9421
Email: David.Hampton@ed.ac.uk |
Course secretary | |
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