通过ChatGPT估计的职业复杂性预测了老年人中超越社会经济地位的认知结果和皮质厚度
Junhong Yu1, Ee-Heok Kua2,3, Rathi Mahendran2,3
1Psychology, School of Social Sciences, Nanyang Technological University, 48 Nanyang Avenue, Singapore, 639798, Singapore. junhong.yu@ntu.edu.sg.
GeroScience
|February 22, 2025
概括
来自ChatGPT的职业复杂性 (OC) 评分有效预测认知衰老和大脑结构,优于传统的社会经济地位 (SES) 指标. 这种新的方法提供了一种简单的方法来分析职位名称以获得认知健康见解.
科学领域:
- 神经科学是一个神经科学.
- 老年学是一门学科.
- 人工智能的人工智能
背景情况:
- 老龄化队列研究通常收集职位数据,但对认知老龄化研究的定量分析具有挑战性.
- 评估职业复杂性的传统方法是劳动密集型的,容易出现人为错误.
- 职位名称与神经认知相关,但由于分析困难,在认知衰老研究中未得到充分利用.
研究的目的:
- 介绍一种新的,简单的方法,使用ChatGPT从职位名称中提取OC评级.
- 评估ChatGPT衍生的OC分数在预测认知和大脑衰老结果中的实用性.
- 为了比较OC得分的预测能力与传统的社会经济地位 (SES) 指标.
主要方法:
- 开发了一个基于ChatGPT的方法,从职位名称中提取OC评级.
- 招募了238名社区老年人 (平均年龄70岁) 进行认知评估和MRI扫描.
- 利用回归模型来预测认知结果,皮质厚度 (CT) 和灰质体积,使用OC分数和SES预测器,控制共变量.
主要成果:
- 来自ChatGPT的OC评分在预测皮质厚度 (CT) 增加和大多数认知结果,包括轻度认知障碍诊断的集群方面表现优于SES指标.
- 即使在控制SES之后,OC得分也显著预测了CT增加和认知结果.
- 超分析解码表明,CT增加发生在与传感运动和记忆处理相关的大脑区域.
结论:
- 来自ChatGPT的OC分数为预测认知和大脑衰老提供了显著和独特的贡献.
- 这种方法提供了一个易于推导的工具,用于增强对认知和大脑衰老结果的预测.
- 这些发现强调了职业复杂性在理解神经认知衰老方面的重要性.
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