LMS Research Internship: Creating and Characterising a Local Myasthenia Gravis cohort
Closing Date: 23/01/2026

Please note: these internships are only available to current undergraduate students in Lancaster University Medical School.

This internship is offered as part of an exciting pilot of research opportunities for Medical School undergraduate students (MBChB and SaES). Each internship will be supervised by an academic member of staff. These opportunities are designed to give students experience of contributing to university research. These internships would be ideal for students considering postgraduate study, intercalated degrees or specialised foundation programmes. 

Project Summary;

This service evaluation aims to characterise the local population of patients with myasthenia gravis (MG) captured within the Trust’s TriNetX node. The objective is to produce a descriptive evidence base that supports local service planning and future quality-improvement initiatives. The work involves no randomisation, no change to patient care, and no intention to generate generalisable findings; all outputs will consist solely of aggregate, de-identified data permitted within TriNetX.

Objectives

The primary aim is to determine the number of patients with MG represented in the Trust’s TriNetX dataset over the last 5–10 years and describe their demographics, comorbidities, treatment patterns, and unplanned admissions. Secondary descriptive aims include producing proxy indicators of outcomes such as 12-month readmission and emergency attendance rates.

Setting, Governance, and Deliverables

All analyses will be conducted within the Trust’s TriNetX environment, using only aggregate counts and platform-approved exports. The project will be registered as a service evaluation with the Trust and will be verified by the local Clinical Audit team. The project will be conducted according to data protection legislation and TriNetX permissions. No row-level (deidentified) data will be extracted.

Within four weeks, the student will deliver:

• A reusable cohort query and associated aggregate tables/figures extracted from TriNetX.
• A 6–8 page internal service evaluation report.
• A one-page executive summary.

Cohort Definition and Variables

The cohort will include adults (≥18 years) with MG, identified by ICD-10 code G70.0, problem list entries, or MG-specific therapies prescribed within the last 10 years. Optional specificity enhancements include filtering by MG-related medications (e.g., pyridostigmine, prednisolone, immunosuppressants, biologics) and relevant procedures (e.g., intravenous immunoglobulin, plasma exchange, thymectomy) while excluding isolated rule-out codes.

Key aggregate variables include:

• Demographics: age band, sex, ethnicity.
• Comorbidities: prevalence of selected chronic diseases and a simple comorbidity count.
• Healthcare utilisation: emergency attendances, non-elective admissions, critical care admissions, length-of-stay bands.
• Therapies: proportion of patients receiving each MG therapy class.
• Outcomes proxies: 12-month all-cause readmission and ED attendance rates.
• Equity indicators: deprivation measures, where available.

Workflow

The student will follow an eight-step workflow: gaining platform access; constructing diagnosis and medication concept sets; building and refining the cohort; generating demographic, comorbidity, and healthcare utilisation summaries; analysing therapy patterns; and exporting permitted aggregates.

Analysis and Outputs

The project will generate a set of descriptive tables and figures, including cohort size by year and demographics, comorbidity burden, therapy use, and patterns of unplanned care. A short narrative will summarise key operational insights. No inferential statistics will be used.

Roles and Risks

Risks include cohort misclassification, sparse data, and time constraints, mitigated through iterative query refinement, aggregation, and adherence to scope. There is a risk of loss of access to TriNetX; if this occurs, the project could be adapted to use a locally collated, de-identified list of patients with the index diagnosis who have been reviewed on an outpatient basis by neurologists working at the Trust (HE and LW).

Major duties:

• The student will conduct the analysis and draft outputs, supported by a clinical supervisor for interpretation and an analytics supervisor for platform guidance.

The successful candidate should be able to demonstrate:

• You must be a current student registered on BSc Sports and Exercise Science, MBChB Surgery, Medicine and Surgery with a Gateway Year and in Years 2-5 of study.
• Strong interest in undertaking research. Previous research experience is not essential as support will be provided, but a willingness to learn is essential.
• Basic Word and Excel proficiency.
• The ability to perform a basic search in search engines such as “PubMed”, “Google Scholar” and other health related databases.
• The ability to read and understand scientific articles and summarise them in a concise and simple manner.

Applicants should have good organisational skills, and academic experience in the following:

• Attending supervisory meetings
  
• Developing a plan of work
  
• Devising search terms and completing literature review
  
• Recording and presenting findings clearly
  
• Scope to develop written review

Please apply through ERS with:

1. A CV including your education history and any work experience (maximum of two full pages) and,
2. A cover letter that explains your interest in research, why you would like to be involved in this particular research internship, and how you meet the criteria outlined in the person specification.

For any informal information about this role please contact Dr Laura White - [email protected].