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相关概念视频

Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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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
Simple...
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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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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...
8.9K
Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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使用自然语言处理预测随机对照试验的样本大小.

Paul Windisch1, Fabio Dennstädt2, Carole Koechli1

  • 1Department of Radiation Oncology, Cantonal Hospital Winterthur, 8400 Winterthur, Switzerland.

JAMIA open
|October 28, 2024
PubMed
概括

开发一个命名实体识别 (NER) 模型,从随机对照试验 (RCT) 摘要中提取样本大小是可行的. 这种NER模型可以改进系统审查和搜索功能.

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在 GPT-4 中使用.基于证据的医学是基于证据的医学.机器学习是机器学习.自然语言处理自然语言处理.随机对照试验是随机对照试验.文本采矿 文本采矿是什么变压器变压器变压器变压器

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科学领域:

  • 医疗信息学 医疗信息学
  • 临床试验分析
  • 自然语言处理自然语言处理.

背景情况:

  • 从随机对照试验 (RCT) 摘要中提取样本大小对于系统性审查和搜索功能至关重要.
  • 当前的方法往往依赖于明确提到样本大小,限制了它们的有效性.
  • 开发自动化方法对于高效的数据提取至关重要.

研究的目的:

  • 开发和验证用于从RCT摘要中提取样本大小信息的新方法.
  • 评估经过训练的命名实体识别 (NER) 模型在预测试验参与者数量的表现.
  • 评估GPT-4o在从RCT摘要中提取样本大小数据中的有效性.

主要方法:

  • 分析了来自高影响力期刊的847个RCT,其中6个实体表明样本大小的标签.
  • 训练了一个命名实体识别 (NER) 模型来提取这些实体.
  • 对于预测准确性,NER模型和GPT-4o在150个RCT的测试组上进行了评估.

主要成果:

  • 最准确的NER模型预测了64.7%的试验的样本大小,与地面真相相比准确率为93.8%.
  • 与地面真相相比,GPT-4o实现了94.7%的预测率,准确率为90.8%.
  • 提取的实体的组合改善了预测模型的性能.

结论:

  • 训练一个NER模型来从摘要中预测RCT样本大小是一个可行的方法.
  • 通过结合各个特征的实体来定制NER模型.
  • 像GPT-4o这样的大型语言模型提供了可比的性能,但可能会产生更高的成本.