Clinical trial · Observational
Research on the Development and Validation of Personalized Exercise Prescriptions 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 (1)
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 |
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
- Average 5-point Likert scores across five expert-defined dimensions-individualization, comprehensiveness, scientific rationality, safety, and executability-are used to compare the performance of fine-tuned models with that of mid-level physicians.
- timeFrame
- From enrollment to completion of prescription evaluation at 1 week
Secondary outcomes (1)
- measure
- Evaluation Form for Consistency Between Model Diagnostic Logic and Medical Consensus
- timeFrame
- From enrollment to completion of prescription evaluation at 1 week
- description
- Measurement Method/Unit: A panel of expert reviewers (at least 3 senior physicians) conducts a blinded assessment of the model's diagnostic reasoning pathways in test cases using a dedicated evaluation form. The outcome is expressed as the mean score (points). Rating Scale: 5-point Likert scale (1=Highly Unsound, 5=Highly Sound) Interpretation of Scores: A higher score indicates better consistency of the model's diagnostic logic with established medical consensus.
Eligibility
Eligibility (as posted)
- Sex
- Female
- 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. * ECOG performance status of 0-1, with adequate physical condition to participate in rehabilitation assessment and exercise prescription activities. * Availability of essential clinical data, including demographic characteristics, tumor stage and subtype, treatment history, and baseline physical fitness assessment. * Able to communicate effectively, maintain stable follow-up contact, and voluntarily participate in evaluation and feedback on exercise prescriptions. 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. * Significant physical or mobility impairments preventing the performance of prescribed exercises. * Severe psychiatric illness or cognitive dysfunction that hinders cooperation with assessments or follow-up. * Incomplete or missing key clinical data, making evaluation or follow-up impossible. * Any other condition deemed inappropriate for participation by the investigators.
References
Publications (0)
Data not yet available