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Related Concept Videos

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences01:20

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Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
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Automated Microbial Diagnostics01:24

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Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...
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Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test01:22

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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...
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Diagnosing Acidosis and Alkalosis01:24

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Diagnosing acid-base imbalances involves systematically analyzing arterial blood samples, focusing on three key measurements: pH, bicarbonate (HCO3−) concentration, and carbon dioxide partial pressure (PCO2). This analysis follows a four-step process that helps identify the imbalance's underlying cause and nature.
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Related Experiment Video

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Bias in clinical chemistry.

Elvar Theodorsson1, Bertil Magnusson, Ivo Leito

  • 1Department of Clinical Chemistry & Department of Clinical & Experimental Medicine, Linköping University, Linköping, Sweden.

Bioanalysis
|December 9, 2014
PubMed
Summary

Clinical chemistry automation reduces variation, but minimizing bias is crucial for accurate patient results. This overview discusses error, uncertainty, and methods to improve measurement accuracy in healthcare.

Area of Science:

  • Clinical chemistry and laboratory medicine.
  • Measurement science and analytical methodology.

Background:

  • Automation in clinical chemistry has significantly improved measurement repeatability and reduced day-to-day variation.
  • Reference measurement systems have partially reduced bias, but further minimization is essential for clinical accuracy.

Purpose of the Study:

  • To present general principles of error and uncertainty concepts in clinical chemistry.
  • To suggest methods for minimizing bias and measurement uncertainty.

Main Methods:

  • Review of error and uncertainty concepts and terminology.
  • Analysis of bias in automated clinical chemistry measurements.
  • Discussion of strategies to reduce bias and uncertainty.

Main Results:

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  • Automation effectively reduces random error components (repeatability, day-to-day variation).
  • Systematic bias remains a challenge, requiring specific strategies for minimization.
  • Small, variable bias can manifest as random error over time, allowing conventional uncertainty calculations.

Conclusions:

  • Minimizing clinically significant bias is vital, especially in multi-laboratory settings analyzing patient samples.
  • Understanding error and uncertainty principles is key to improving measurement accuracy.
  • Implementing suggested methods can enhance healthcare outcomes by ensuring reliable diagnostic information.