Research on the Development and Validation of Personalized Exercise Prescription System for Breast Cancer Patients Based on Large Language Models
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.
調査の概要
状態
状態
研究の種類
研究の種類
入学 (推定)
入学
連絡先と場所
研究場所
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Guangdong
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Guangzhou、Guangdong、中国、510000
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
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コンタクト:
- jianli Zhao
- 電話番号:15920589334
- メール:zhaojianli1988@126.com
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コンタクト:
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参加基準
適格基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
サンプリング方法
調査対象母集団
説明
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.
研究計画
研究はどのように設計されていますか?
デザインの詳細
グループ/コホートの数
コホートと介入
グループ/コホートグループ/コホート |
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Postoperative breast cancer patients receiving LLM-based exercise prescription evaluation
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.
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この研究は何を測定していますか?
主要な結果の測定
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
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Overall Quality Score of Exercise Prescriptions Based on a Five-Dimensional Expert Evaluation Framework
時間枠:From enrollment to completion of prescription evaluation at 1 week
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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.
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From enrollment to completion of prescription evaluation at 1 week
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二次結果の測定
二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
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Subgroup-Specific Scientific Rationale and Safety Scores of Model-Generated Exercise Prescriptions
時間枠:From enrollment to completion of prescription evaluation at 1 week
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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.
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From enrollment to completion of prescription evaluation at 1 week
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協力者と研究者
研究記録日
主要日程の研究
研究開始 (推定)
研究開始
一次修了 (推定)
一次修了
研究の完了 (推定)
研究の完了
試験登録日
最初に提出
最初に提出
QC基準を満たした最初の提出物
QC基準を満たした最初の提出物
最初の投稿 (実際)
最初の投稿
学習記録の更新
投稿された最後の更新 (実際)
投稿された最後の更新
QC基準を満たした最後の更新が送信されました
QC基準を満たした最後の更新が送信されました
最終確認日
最終確認日
詳しくは
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