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Research on the Development and Validation of Personalized Exercise Prescriptions for Breast Cancer Patients Based on Large Language Models

1. juni 2026 opdateret af: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

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.

Studieoversigt

Status

Aktiv, ikke rekrutterende

Betingelser

Undersøgelsestype

Observationel

Tilmelding (Anslået)

220

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiesteder

  • Kina
    • Guangdong
      • Guangzhou, Guangdong, Kina, 510000
        • Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ingen

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

Patients with breast cancer who have completed primary surgery and entered the postoperative rehabilitation stage at Sun Yat-sen Memorial Hospital, Sun Yat-sen University (Guangzhou, China).All participants receive individualized exercise prescriptions generated by large language models under physician supervision and approval, and their feedback on the feasibility of these prescriptions is collected.

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.
  • 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.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
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.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Tidsramme
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.
Tidsramme: From enrollment to completion of prescription evaluation at 1 week
From enrollment to completion of prescription evaluation at 1 week

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Evaluation Form for Consistency Between Model Diagnostic Logic and Medical Consensus
Tidsramme: From enrollment to completion of prescription evaluation at 1 week

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

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

1. oktober 2025

Primær færdiggørelse (Anslået)

1. september 2026

Studieafslutning (Anslået)

1. juli 2027

Datoer for studieregistrering

Først indsendt

25. november 2025

Først indsendt, der opfyldte QC-kriterier

1. juni 2026

Først opslået (Faktiske)

2. juni 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

2. juni 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

1. juni 2026

Sidst verificeret

1. juni 2026

Mere information

Begreber relateret til denne undersøgelse

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • SYSKY-2025-786-01

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