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相关概念视频

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Quantifying Mixing using Magnetic Resonance Imaging
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AppRaise:使用后置混合模型量化系统审查中的证据不确定性的软件.

Conrad Kabali1,2

  • 1Health Technology Assessment Unit, Acute and Hospital-Based Care Portfolio, Ontario Health, Toronto, Ontario, Canada.

Journal of evaluation in clinical practice
|September 10, 2025
PubMed
概括

本研究介绍了AppRaise,这是一个免费软件工具,可以量化系统审查中的偏见. AppRaise通过提供统一的不确定性衡量标准来改善决策,从而增强基于证据的医疗保健.

关键词:
这就是AppRaise的价值观.决策能力 决策能力卫生技术评估 卫生技术评估后部混合物模型的模型.量化不确定性的量化.系统性审查 系统性审查

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科学领域:

  • 医疗信息学 医疗信息学
  • 生物统计学 生物统计学
  • 卫生技术评估 卫生技术评估

背景情况:

  • 系统性审查对于基于证据的医疗保健至关重要,但量化偏见仍然具有挑战性.
  • 当前的方法往往依赖于叙事评估,缺乏定量严谨性.
  • 时间限制和技术困难阻碍了证据评估人员对偏见的全面评估.

研究的目的:

  • 开发一种定量方法来评估系统审查中的偏见和随机错误.
  • 推出AppRaise,一个免费的,基于Web的软件,实现一个用于偏差评估的后置混合模型.

主要方法:

  • 开发了一个后置混合模型,将随机错误和偏差整合到统一的不确定性测量中.
  • 创建AppRaise软件是为了提供用户友好的访问这种定量方法.
  • 该方法应用于关于持续血糖监测 (CGM) 的卫生技术评估 (HTA) 报告.

主要成果:

  • 应用AppRaise的HTA报告显示,CGM在1型糖尿病中降低A1c水平的概率为86%.
  • 结果与系统审查中的其他定量偏差调整方法相比.
  • 该软件使得研究结果具有很高的确定性.

结论:

  • AppRaise为验证证据质量和评估系统审查中对偏见的敏感性提供了一个有价值的工具.
  • 它可以独立使用或作为质量评分方法的补充.
  • 该软件有助于基于证据的医疗保健的强有力的决策.