Preliminary Evaluation of a Large Language Model-Based Tool for Complex Surgical Decision Support in Lung Cancer
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 8
- 试验地点
- 1
研究概览
简要总结
This study is an exploratory effect-size estimation study, with the following specific objectives: ① to estimate the point estimate and 95% confidence interval of the Win Ratio for the experimental group (GAPS-Agent) versus the control group (large language model) in blinded pairwise preference judgments by thoracic surgery expert adjudicators, to serve as a sample size planning parameter for subsequent multicenter confirmatory clinical trials; ② to preliminarily evaluate the value of GAPS-Agent within clinical workflows.The hypothesis of this study is as follows: compared with a general-purpose large language model without medical enhancement (control group), a structured agentic workflow optimized on the basis of the GAPS evaluation framework (GAPS-Agent, experimental group) can help junior resident physicians generate clinical decision plans for complex lung cancer cases that are more strongly preferred by senior thoracic surgery expert adjudicators.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Other
- 盲法
- Single (Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 65 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Resident Physician Subjects:
- •Holds a valid and legally effective Physician Practice License of the People's Republic of China;
- •Currently holds the rank of resident physician in a thoracic surgery department at a tertiary Class A (3A) hospital;
- •Agrees to complete all assessment tasks of the main study phase in accordance with the study protocol;
- •Can guarantee the time and effort required to complete all assessment tasks of the main study.
- •Study Cases:
- •The case was discussed at the Thoracic Oncology Multidisciplinary Team (MDT) conference of Peking University People's Hospital between January 2025 and May 2026;
- •The current version of the NCCN guidelines does not provide an explicit recommendation covering the management of the case;
- •Does not overlap with the GAPS evaluation set;
- •The case is presented in pure text in a structured format, with all direct and indirect identifiers removed and complete de-identification performed prior to inclusion;
- •From the pool of eligible cases, 12 cases will be randomly drawn using Python (numpy.random, with a fixed and archived seed) to serve as the main study cases. The cases will cover 6 themes (chest mass of undetermined diagnosis, early-stage lung cancer, locally advanced lung cancer, oligometastatic/oligoprogressive disease, special intraoperative situations, and tumor recurrence), with 1 - 4 cases per theme.
- •Adjudication Expert Panel:
- •Holds a valid and legally effective Physician Practice License of the People's Republic of China;
- •Currently holds the rank of attending physician or above in a thoracic surgery department at a tertiary Class A hospital;
- •Chairs or regularly participates in lung cancer multidisciplinary team (MDT) work in their department.
排除标准
- •Resident Physician Subjects:
- •Has previously participated in the construction of the GAPS evaluation set or the development of GAPS-Agent;
- •Unable to complete the tasks of the study phase.
- •Study Cases:
- •Key case information is missing, such as text-form data on pathology (including IHC/NGS), imaging, laboratory tests, prior medical history, comorbidities, or PS score;
- •Decision-making for the case is strictly dependent on non-text information.
- •Adjudication Expert Panel:
- •Participated in the construction of the GAPS evaluation set, the content validity verification, or the development of GAPS-Agent for this study;
- •Has a direct conflict of interest with any specific product among the two-arm tools of this study.
研究组 & 干预措施
control arm
LLM
干预措施: LLM (Other)
test arm
GAPS-Agent
干预措施: GAPS-Agent (Other)
研究者
XiuYuan Chen
Associate Chief Physician
Peking University People's Hospital