Research on the Development and Validation of Personalized Exercise Prescriptions 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.
Aperçu de l'étude
Statut
Les conditions
Type d'étude
Inscription (Estimé)
Contacts et emplacements
Lieux d'étude
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Guangdong
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Guangzhou, Guangdong, Chine, 510000
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
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Critères de participation
Critère d'éligibilité
Âges éligibles pour étudier
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
Méthode d'échantillonnage
Population étudiée
La description
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.
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
Cohortes et interventions
Groupe / Cohorte |
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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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Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Délai |
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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.
Délai: From enrollment to completion of prescription evaluation at 1 week
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From enrollment to completion of prescription evaluation at 1 week
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Mesures de résultats secondaires
Mesure des résultats |
Description de la mesure |
Délai |
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Evaluation Form for Consistency Between Model Diagnostic Logic and Medical Consensus
Délai: From enrollment to completion of prescription evaluation at 1 week
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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. |
From enrollment to completion of prescription evaluation at 1 week
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Collaborateurs et enquêteurs
Dates d'enregistrement des études
Dates principales de l'étude
Début de l'étude (Réel)
Achèvement primaire (Estimé)
Achèvement de l'étude (Estimé)
Dates d'inscription aux études
Première soumission
Première soumission répondant aux critères de contrôle qualité
Première publication (Réel)
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière vérification
Plus d'information
Termes liés à cette étude
Termes MeSH pertinents supplémentaires
Autres numéros d'identification d'étude
- SYSKY-2025-786-01
Informations sur les médicaments et les dispositifs, documents d'étude
Étudie un produit pharmaceutique réglementé par la FDA américaine
Étudie un produit d'appareil réglementé par la FDA américaine
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