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.
Studieoversikt
Status
Status
Forhold
Forhold
Studietype
Studietype
Registrering (Antatt)
Registrering
Kontakter og plasseringer
Studiesteder
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Guangdong
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Guangzhou, Guangdong, Kina, 510000
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
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Ta kontakt med:
- jianli Zhao
- Telefonnummer: 15920589334
- E-post: zhaojianli1988@126.com
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Ta kontakt med:
- E-post: zhaojianli1988@126.com
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Deltakelseskriterier
Kvalifikasjonskriterier
Kvalifikasjonskriterier
Alder som er kvalifisert for studier
- Voksen
- Eldre voksen
Tar imot friske frivillige
Prøvetakingsmetode
Studiepopulasjon
Beskrivelse
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.
Studieplan
Hvordan er studiet utformet?
Designdetaljer
Antall grupper / kohorter
Kohorter og intervensjoner
Gruppe / KohortGruppe / Kohort |
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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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Hva måler studien?
Primære resultatmål
Primære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
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Overall Quality Score of Exercise Prescriptions Based on a Five-Dimensional Expert Evaluation Framework
Tidsramme: 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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Sekundære resultatmål
Sekundære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
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Subgroup-Specific Scientific Rationale and Safety Scores of Model-Generated Exercise Prescriptions
Tidsramme: 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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Samarbeidspartnere og etterforskere
Sponsor
Sponsor
Studierekorddatoer
Studer hoveddatoer
Studiestart (Antatt)
Studiestart
Primær fullføring (Antatt)
Primær fullføring
Studiet fullført (Antatt)
Studiet fullført
Datoer for studieregistrering
Først innsendt
Først innsendt
Først innsendt som oppfylte QC-kriteriene
Først innsendt som oppfylte QC-kriteriene
Først lagt ut (Faktiske)
Først lagt ut
Oppdateringer av studieposter
Sist oppdatering lagt ut (Faktiske)
Sist oppdatering lagt ut
Siste oppdatering sendt inn som oppfylte QC-kriteriene
Siste oppdatering sendt inn som oppfylte QC-kriteriene
Sist bekreftet
Sist bekreftet
Mer informasjon
Begreper knyttet til denne studien
Ytterligere relevante MeSH-vilkår
Andre studie-ID-numre
Andre studie-ID-numre
- SYSKY-2025-786-02
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