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Updated: Jan 7, 2026

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Conceptualization of Risk Stratification Using Large Language Models to Predict Severe Mycoplasma pneumoniae

Adebanke Adeyemi1, Swapan Nath2

  • 1College of Medicine, Anne Burnett Marion School of Medicine, Texas Christian University, Fort Worth, USA.

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|January 2, 2026
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Summary

This study uses large language models (LLMs) to create a risk framework for severe Mycoplasma pneumoniae pneumonia (MPP). LLM-assisted synthesis of a single case helps identify high-risk predictors for educational purposes.

Keywords:
artificial intelligence and educationchatgptclinical reasoning skillshealth-professions educationlarge language modelsnecrotizing pneumoniapredictive markers for severity

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Area of Science:

  • Medical Informatics
  • Pulmonology
  • Medical Education

Background:

  • Large language models (LLMs) show promise in healthcare for literature synthesis and decision support.
  • The role of LLMs in health professions education scholarship is an emerging area.
  • Predicting severe Mycoplasma pneumoniae pneumonia (MPP) in immunocompetent adults is challenging due to variable host factors.

Purpose of the Study:

  • To examine predictive features of severe MPP.
  • To explore LLM utilization in developing a conceptual risk stratification framework.
  • To enhance health professions education scholarship and research skills development.

Main Methods:

  • Analysis of a single necrotizing MPP case in a 32-year-old woman.
  • Structured literature review (2010-2024) to identify MPP severity predictors.
  • LLM-assisted synthesis to organize predictors into a three-tier risk framework (low, moderate, high).

Main Results:

  • The case presented high-risk predictors: cavitary disease, loculated effusion, and pneumothorax.
  • LLM synthesis mapped these findings to a high-risk tier within the conceptual framework.
  • Demonstrated LLM's ability to transform a single case into a structured, hypothesis-generating tool.

Conclusions:

  • LLM-assisted synthesis can link case data with predictors for early recognition of severe MPP.
  • This approach offers a replicable educational method for developing AI literacy and research skills.
  • Conclusions are theoretical and hypothesis-generating, requiring empirical validation in larger cohorts.