Research on the Development and Validation of Personalized Exercise Prescription System for Breast Cancer Patients Based on Large Language Models

Condition(s) studied

Study summary

The goal of this observational study is to develop and evaluate a large language model (LLM)-based decision support system for exercise prescription in breast cancer patients, aiming to provide personalized decision-making support for postoperative breast cancer rehabilitation.

The main questions it aims to answer are:

How accurate, personalized, and safe are the exercise prescriptions generated by the fine-tuned LLM? How does the model's performance compare with other mainstream or non-fine-tuned models across different stages and subtypes of breast cancer? Participants are postoperative breast cancer rehabilitation patients treated at Sun Yat-sen Memorial Hospital of Sun Yat-sen University. They will have demographic, tumor, treatment, and physical fitness data collected; receive personalized exercise prescriptions automatically generated by the LLM-based system; and provide subjective evaluations on the feasibility and executability of the prescriptions.

Eligibility

Primary outcome measure(s)

• Overall Quality Score of Exercise Prescriptions Based on a Five-Dimensional Expert Evaluation Framework — From enrollment to completion of prescription evaluation at 1 week
  Each exercise prescription will be independently evaluated by six multidisciplinary experts across five dimensions: scientific rationale, personalization, comprehensiveness, safety, and feasibility. Each dimension will be rated on a 5-point Likert scale from 1 to 5. The five dimension scores will be summed to generate an overall quality score ranging from 5 to 25, with higher scores indicating better overall prescription quality. For each prescription, the mean overall score across the six experts will be used for analysis.

Trial sites (1)

More Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University trials in China

Other trials for the same condition