Breast Cancer, Exercise and Recovery
Conditions
Brief 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.
Interventions
None listed
Sponsors
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
* Adult patients aged 18-75 years with early-stage breast cancer who have undergone surgical treatment, such as mastectomy or breast-conserving surgery. * The patients had clear clinical diagnosis and complete electronic medical record information (including demographic information, tumor stage and classification, treatment history, physical performance evaluation data, etc.).
Exclusion criteria
* Presence of severe postoperative complications or comorbidities (e.g., uncontrolled cardiac or pulmonary disease) that may interfere with participation in rehabilitation or pose a safety risk.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Subgroup-Specific Scientific Rationale and Safety Scores of Model-Generated Exercise Prescriptions | From enrollment to completion of prescription evaluation at 1 week | Model performance will be evaluated across predefined subgroups based on age, breast cancer stage, molecular subtype, surgical procedure, and treatment modality. Scientific rationale and safety will each be rated on a 1-5 Likert scale, with higher scores indicating better performance. Differences across models and subgroups will be assessed using two-way ANOVA or generalized linear models, including interaction terms between model type and patient characteristics. |
Countries
China
Outcome results
None listed