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相关概念视频

Language and Cognition01:27

Language and Cognition

339
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Law of Effect01:06

Law of Effect

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B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
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Group Design02:01

Group Design

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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相关实验视频

Updated: Jun 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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大型语言模型对管理推理的影响:一个随机对照试验.

Ethan Goh1,2, Robert Gallo3, Eric Strong4

  • 1Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA.

medRxiv : the preprint server for health sciences
|August 16, 2024
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概括

大型语言模型 (LLM) 人工智能 (AI) 与传统资源相比,显著改善了医生管理推理. 这种人工智能协助增强了临床决策,特别是在复杂的患者病例中.

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科学领域:

  • 人工智能在医学中的应用
  • 临床决策支持系统 临床决策支持系统
  • 医学教育和培训 医学教育和培训

背景情况:

  • 大型语言模型 (LLM) 显示出诊断推理的潜力,但它们对复杂的管理决策的影响尚不清楚.
  • 医生在开放式的临床管理任务中的表现通常依赖于从各种来源合成信息.

研究的目的:

  • 评估LLM辅助是否提高了医生在开放式临床管理推理任务中的表现.
  • 为了比较LLM增强资源与单独使用传统资源的有效性.

主要方法:

  • 一项前性随机对照试验,涉及多个机构92名医生 (临床医生和住院医生).
  • 参与者使用GPT-4 (通过ChatGPT Plus) 与传统资源或仅使用传统资源来管理专家开发的五个临床病例小组.
  • 通过Delphi过程创建分数标签,以评估管理和诊断决策.

主要成果:

  • 使用LLM辅助的医生获得了明显更高的整体得分 (增加6.5%,p<0.001).
  • 在管理 (6.1%),诊断 (12.1%) 和具体案例 (6.2%) 决策领域,有所改善.
  • 每个案例的LLM用户花费的时间更长 (119.3秒),GPT-4增强组和GPT-4单独组之间没有显著差异.

结论:

  • 通过LLM辅助,可以明显改善医生管理的推理,特别是在情境和患者特定的决策方面.
  • 这些发现表明,LLM可以作为增强复杂医疗场景中的临床管理的宝贵工具.
  • 像LLM这样的AI工具的整合有望提高医生的表现和患者护理.