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Related Concept Videos

Quality Control01:05

Quality Control

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
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Quality Assurance01:19

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Data Validation01:15

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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:
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Data Validation01:03

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Statistical Analysis: Overview01:11

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Health Information Technology and Healthcare Information System01:30

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Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
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Data Standardization and Quality Management.

Paul A Lapchak1, John H Zhang2

  • 1Translational Research, Department of Neurology & Neurosurgery, Cedars-Sinai Medical Center, Advanced Health Sciences Pavilion, Suite 8305, 127 S. San Vicente Blvd, Los Angeles, CA, 90048, USA. Paul.Lapchak@cshs.org.

Translational Stroke Research
|March 12, 2017
PubMed
Summary

Improving scientific research transparency is crucial for stroke patient treatments. This study proposes guidelines for data management and archival to enhance reliability and comparability in translational stroke research.

Keywords:
Animal researchBrainClinical trialCytoprotectionDrug discoveryEmbolicHemorrhageNIHSSNeuroprotectionNeuroprotectiveRIGORSTAIRStrokeTranslationalTransparency

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Area of Science:

  • Neuroscience
  • Biomedical Research
  • Translational Science

Background:

  • Scientific research, particularly in stroke treatment development, faces challenges with data transparency despite existing guidelines.
  • There's a growing need for improved transparency and data management in translational stroke research to accelerate therapeutic discovery.

Purpose of the Study:

  • To address the critical need for enhanced data transparency and management in translational stroke research.
  • To provide resources and guidelines for researchers to achieve Good Laboratory Practices (GLP) compliance.
  • To propose a standardized approach for data archival, making translational research data comparable to clinical standards.

Main Methods:

  • Identification of resources for Good Laboratory Practices (GLP) compliance.
  • Development of guidelines for accurate data management and archival.
  • Proposal of steps for preparing research data for archival purposes.

Main Results:

  • The document outlines resources and guidelines to improve data transparency and management in translational stroke research.
  • It proposes a framework for data archival to ensure comparability and reliability across studies.
  • The guidelines aim to facilitate the translation of research findings into effective treatments for stroke patients.

Conclusions:

  • Implementing standardized data management and archival practices is essential for advancing translational stroke research.
  • Improved data transparency and reliability will accelerate the discovery and development of new stroke therapies.
  • Adherence to proposed guidelines can lead to more comparable and dependable research data, benefiting stroke patient care.