Back
For best output, select "Paper Size" as "A4" and "Margin" as "0" or "None".
To save or print to PDF, please select Print Destination > Save as PDF, enable Background Graphics under "More Settings", then click "Save".
During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
A major barrier to actualizing precision medical education is performing the ongoing, continuous analysis necessary for assessment and iterative feedback to improve foundational knowledge and diagnostic reasoning. We are leveraging large language models (LLMs) in this pilot project to analyze nephrology fellow clinical documentation and map their diagnostic exposures to topics relevant to the practice of nephrology with the goal of providing subsequent targeted educational interventions based on each individual learner’s needs.
50 nephrology fellow hyponatremia clinical encounters (47 inpatient and 3 outpatient) at a large academic medical center were extracted into a HIPAA compliant secure computing environment. These encounters were analyzed by two expert reviewers and by pre-trained LLMs including MedGemma, Qwen2.5, and LLaMA3. We determined the underlying hyponatremia diagnoses present and mapped them to the ABIM nephrology blueprint. We evaluated clinical reasoning utilizing a validated tool (R-IDEA). Expert reviewer results were used as the “gold standard” and compared to LLM output to evaluate LLM performance. Cohen’s kappa for inter-rater agreement was determined for hyponatremia diagnoses and Spearman correlation and Pearson correlation were determined for each R-IDEA clinical reasoning category.
Expert reviewers identified SIADH (11), hypervolemic hyponatremia (17), low solute intake (4), hyponatremia due to thiazide diuretic use (3), hypertonic hyponatremia (2), pseudohyponatremia (1), and hypotonic hyponatremia due to other causes (24) after manual review. LLM performance varied by model and across hyponatremia diagnoses. We found that Qwen2.5 performed best at this stage. Inter-rater reliability between expert reviewers and Qwen2.5 was moderate (Cohen’s k 0.56). Correct identification by the LLM occurred most frequently for SIADH and least frequently for hypotonic hyponatremia due to thiazide diuretic use. We found weak agreement at this stage between LLM R-IDEA score and expert reviewers. Spearman correlation for total R-IDEA score was 0.361 and Pearson correlation was 0.320.
This innovative use of LLMs is an initial proof of concept project that strives to improve nephrology fellow education via analysis of learner’s real-world documentation with plans for subsequent targeted educational interventions to meet learners needs and improve clinical reasoning. We have demonstrated modest agreement between expert reviewers and readily available LLMs regarding hyponatremia diagnoses present and weak agreement when evaluating learner clinical reasoning. Continued efforts to optimize model performance are underway. Subsequent piloting of delivery of targeted educational interventions for learners based on real time evaluation of this data and scaling this system throughout the nephrology curriculum are planned next steps to enable continuous individualized learning throughout nephrology fellowship that is tailored to a specific fellow’s needs.
