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- Klinische proef NCT07767565
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.
Studie Overzicht
Toestand
Conditie
Studietype
Inschrijving (Geschat)
Contacten en locaties
Studie Locaties
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Guangdong
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Guangzhou, Guangdong, China, 510000
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
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Contact:
- jianli Zhao
- Telefoonnummer: 15920589334
- E-mail: zhaojianli1988@126.com
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Contact:
- E-mail: zhaojianli1988@126.com
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Deelname Criteria
Geschiktheidscriteria
Leeftijden die in aanmerking komen voor studie
- Volwassen
- Oudere volwassene
Accepteert gezonde vrijwilligers
Bemonsteringsmethode
Studie Bevolking
Beschrijving
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.
Studie plan
Hoe is de studie opgezet?
Ontwerpdetails
Cohorten en interventies
Groep / Cohort |
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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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Wat meet het onderzoek?
Primaire uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
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Overall Quality Score of Exercise Prescriptions Based on a Five-Dimensional Expert Evaluation Framework
Tijdsspanne: 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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Secundaire uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
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Subgroup-Specific Scientific Rationale and Safety Scores of Model-Generated Exercise Prescriptions
Tijdsspanne: 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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Medewerkers en onderzoekers
Studie record data
Bestudeer belangrijke data
Studie start (Geschat)
Primaire voltooiing (Geschat)
Studie voltooiing (Geschat)
Studieregistratiedata
Eerst ingediend
Eerst ingediend dat voldeed aan de QC-criteria
Eerst geplaatst (Werkelijk)
Updates van studierecords
Laatste update geplaatst (Werkelijk)
Laatste update ingediend die voldeed aan QC-criteria
Laatst geverifieerd
Meer informatie
Termen gerelateerd aan deze studie
Aanvullende relevante MeSH-voorwaarden
Andere studie-ID-nummers
- SYSKY-2025-786-02
Informatie over medicijnen en apparaten, studiedocumenten
Bestudeert een door de Amerikaanse FDA gereguleerd geneesmiddel
Bestudeert een door de Amerikaanse FDA gereguleerd apparaatproduct
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