Clinical trial · Observational
Research on the Development and Validation of Personalized Exercise Prescription System for Breast Cancer Patients Based on Large Language Models
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
| Exercise and Recovery | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (1)
- label
- Postoperative breast cancer patients receiving LLM-based exercise prescription evaluation
- description
- Postoperative breast cancer patients at Sun Yat-sen Memorial Hospital will have clinical and physical data collected. Each patient receives an exercise prescription generated by a fine-tuned large language model (LLM)-based decision support system and provides feedback on its feasibility.
Primary outcomes (1)
- measure
- Overall Quality Score of Exercise Prescriptions Based on a Five-Dimensional Expert Evaluation Framework
- timeFrame
- From enrollment to completion of prescription evaluation at 1 week
- description
- 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 outcomes (1)
- measure
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available