利用历史数据推导参考极限 - - 电子规范与传统推导参考极限的比较
Ø Dunker1,2,3, T S Szczepanski3, H O P Do3
1Department of Research and Innovation, Division of Neuroscience, Oslo University Hospital, Oslo, Norway.
Clinical neurophysiology practice
|May 6, 2024
概括
外推标准 (e-norms) 方法准确地从历史数据中生成神经传导研究 (NCS) 参考极限,与儿童和成人传统方法非常相匹配. 这种方法增强了NCS结果的解释,提高了诊断准确度.
科学领域:
- 临床神经生理学 临床神经生理学
- 生物统计学 生物统计学
背景情况:
- 神经传导研究 (NCS) 对于诊断神经系统疾病至关重要.
- 准确的参考极限对于解释NCS结果至关重要.
- 传统的参考极限推导需要从健康个体广泛收集数据.
研究的目的:
- 进一步开发和验证外加推算规范 (e-norms) 方法来导出NCS参考极限.
- 为了在儿科和成人群体中比较电子规范的衍生极限与传统的参考极限.
- 评估使用历史实验室数据集来生成NCS参考极限的可行性.
主要方法:
- 电子规范方法应用于历史NCS数据集.
- 使用±2 SD和±2.5 SD计算了参考极限.
- 结果与来自健康对照组 (65名儿童,578名成年人) 的参考限值进行了比较.
- 分析了五个常见的神经的幅度,传导速度和F波.
主要成果:
- 电子规范方法 (平均±2SD) 产生了比传统方法略微更严格的参考限值.
- 将e-norms范围调整为±2.5 SD的平均值,改善了儿科结果,并在成年人中稍微过度纠正.
- 电子规范与传统方法之间的平均一致性为95% (±2 SD) 和96% (±2.5 SD).
结论:
- 适应的e-norms方法可靠地复制了传统的NCS参考值.
- 该方法适用于分析大型历史NCS数据集,减少偏差并提高评级准确性.
- 电子标准为生成参考限值提供了一个可行的替代方案,特别是当新的数据收集不切实际时.
更多相关视频
14:13Detecting Estrogenic Ligands in Personal Care Products using a Yeast Estrogen Screen Optimized for the Undergraduate Teaching Laboratory
Published on: January 1, 2018
13.1K
08:56Detection of Regulated Ergot Alkaloids in Food Matrices by Liquid Chromatography-Trapped Ion Mobility Spectrometry-Time-of-Flight Mass Spectrometry
Published on: November 22, 2024
612
相关概念视频
02:59The Nernst Equation
40.7K
Nonstandard Reaction Conditions
The interconnection between standard cell potentials and various thermodynamic parameters such as the standard free energy change ΔG° and equilibrium constant K has been previously explored. For example, a redox reaction involving zinc(II) and tin(II) ions at 1 M concentration with Eºcell = +0.291 V and ΔG° = −56.2 kJ is spontaneous.
The interconnection between standard cell potentials and various thermodynamic parameters such as the standard free energy change ΔG° and equilibrium constant K has been previously explored. For example, a redox reaction involving zinc(II) and tin(II) ions at 1 M concentration with Eºcell = +0.291 V and ΔG° = −56.2 kJ is spontaneous.
40.7K
01:05Difference from Background: Limit of Detection
6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
6.4K
01:17Chemical Shift: Internal References and Solvent Effects
630
In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
630
01:15Data Validation
161
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
161
01:16Contaminants and Errors
89
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Another key consideration is determining the appropriate number of samples required to...
89
01:00Detection of Gross Error: The Q Test
6.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.1K