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.
Studienübersicht
Status
Bedingungen
Studientyp
Einschreibung (Geschätzt)
Kontakte und Standorte
Studienorte
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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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Teilnahmekriterien
Zulassungskriterien
Studienberechtigtes Alter
- Erwachsene
- Älterer Erwachsener
Akzeptiert gesunde Freiwillige
Probenahmeverfahren
Studienpopulation
Beschreibung
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.
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
Kohorten und Interventionen
Gruppe / Kohorte |
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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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Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Zeitfenster |
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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.
Zeitfenster: 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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Sekundäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
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Evaluation Form for Consistency Between Model Diagnostic Logic and Medical Consensus
Zeitfenster: 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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Mitarbeiter und Ermittler
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Tatsächlich)
Primärer Abschluss (Geschätzt)
Studienabschluss (Geschätzt)
Studienanmeldedaten
Zuerst eingereicht
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst gepostet (Tatsächlich)
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Zusätzliche relevante MeSH-Bedingungen
Andere Studien-ID-Nummern
- SYSKY-2025-786-01
Arzneimittel- und Geräteinformationen, Studienunterlagen
Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt
Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt
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