Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.2K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.2K
Variability: Analysis01:11

Variability: Analysis

598
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
598
Random and Systematic Errors01:20

Random and Systematic Errors

15.7K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
15.7K
Dosage Regimen: Individualization01:24

Dosage Regimen: Individualization

245
Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
245
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

67
The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
67
Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test01:22

Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test

245
In clinical practice, the direct measurement of hepatic blood flow to evaluate liver function presents significant challenges due to the intricate and specialized nature of the necessary techniques. Consequently, healthcare professionals often rely on empirical estimates derived from thorough patient examinations and liver function tests to gauge liver health. Among the tools at their disposal, the Child–Pugh and MELD scoring systems stand out for their ability to categorize and assess...
245

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Variability of Spleen and Mesenteric Lymph Node in Control Cynomolgus Monkeys ( Macaca fascicularis) from Nonclinical Safety Studies: A Retrospective Assessment.

Toxicologic pathology·2018
Same author

Scientific and Regulatory Policy Committee Points to Consider: Data Visualization for Clinical and Anatomic Pathologists.

Toxicologic pathology·2018
Same author

Serum perfluorooctanoic acid (PFOA) concentrations in normal and hyperlipidemic female hamsters dosed orally with ammonium perfluorooctanoate (APFO) for up to 30 days.

Toxicology reports·2017
Same author

Deciphering Sources of Variability in Clinical Pathology.

Toxicologic pathology·2016
Same author

Is It Adverse, Nonadverse, Adaptive, or Artifact?

Toxicologic pathology·2016
Same author

Maturity-related Variability of the Thymus in Cynomolgus Monkeys (Macaca fascicularis).

Toxicologic pathology·2016

Related Experiment Video

Updated: Mar 7, 2026

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay
14:45

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay

Published on: September 16, 2012

15.6K

Deciphering Sources of Variability in Clinical Pathology-It's Not Just about the Numbers.

Nancy E Everds1

  • 11 Amgen Inc., South San Francisco, California, USA.

Toxicologic Pathology
|February 10, 2017
PubMed
Summary

Controlling preanalytical variables is crucial for accurate clinical pathology results in nonclinical safety studies. Careful attention to factors like diet and handling improves data quality and study value.

Keywords:
clinical chemistryhematologypreanalyticalrestraint

More Related Videos

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
05:58

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

4.6K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.5K

Related Experiment Videos

Last Updated: Mar 7, 2026

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay
14:45

Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay

Published on: September 16, 2012

15.6K
Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
05:58

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

4.6K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.5K

Area of Science:

  • Veterinary Pathology
  • Toxicology
  • Laboratory Medicine

Background:

  • Preanalytical variables significantly impact clinical pathology parameters in nonclinical safety and toxicity studies.
  • These variables can complicate data interpretation, raise regulatory concerns, and obscure the effects of test articles.

Purpose of the Study:

  • To highlight the importance of controlling preanalytical variables in nonclinical safety studies.
  • To identify key factors that influence clinical pathology results.
  • To emphasize the benefits of minimizing preanalytical variability for data integrity.

Main Methods:

  • Review of factors influencing preanalytical variability in animal studies.
  • Emphasis on controlled variables including animal diet, restraint, anesthesia, and collection timing.
  • Importance of animal technician proficiency in sample collection.

Main Results:

  • Preanalytical variables can significantly alter clinical pathology parameters.
  • Controlled factors include diet, anesthesia, procedures, collection timing, and technician skill.
  • Failure to control these variables can lead to interpretation challenges and regulatory issues.

Conclusions:

  • Controlling preanalytical variables is essential for reliable nonclinical safety study results.
  • Knowledge of influencing factors allows for effective minimization of variability.
  • Improved control enhances data quality and maximizes study value.