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Related Concept Videos

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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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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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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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Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
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When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
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Studying the Protein Quality Control System of D. discoideum Using Temperature-controlled Live Cell Imaging
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Quality control optimization part I: Metrics for evaluating predictive performance of quality control.

Robert L Schmidt1, Lauren N Pearson2

  • 1The Department of Pathology, University of Utah, Salt Lake City, UT, United States of America; ARUP Laboratories, Salt Lake City, UT, United States of America.

Clinica Chimica Acta; International Journal of Clinical Chemistry
|April 12, 2019
PubMed
Summary

Quality control (QC) policies can be improved by evaluating them from an end-user perspective. New metrics, like positive predictive value (PPV) and negative predictive value (NPV), help assess QC effectiveness in real-world assay error scenarios.

Keywords:
ErrorNegative predictive valuePositive predictive valueQuality controlStatistics

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Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Statistical Quality Control

Background:

  • Traditional quality control (QC) policy design uses power curves, reasoning from cause to effect.
  • End-users require metrics that reason from effect (QC signal) to cause (assay error).
  • Evaluating QC policies from an end-user perspective is crucial for practical application.

Purpose of the Study:

  • To develop and evaluate metrics for assessing QC policies from an end-user viewpoint.
  • To analyze QC policy performance using common accuracy metrics.
  • To explore the impact of design choices on QC policy effectiveness.

Main Methods:

  • Developed a dichotomous model classifying assay errors as important or unimportant based on a critical shift size (Sc).
  • Applied accuracy metrics: sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) to analyze QC policies.
  • Investigated the influence of QC limits and number of repeats on performance measures.

Main Results:

  • Positive predictive value (PPV) demonstrated significant variability (1%–100%) depending on the context.
  • Negative predictive value (NPV) also varied (40%–100%) but was less context-dependent than PPV.
  • Adjusting QC limits or the number of repeats can enhance PPV and NPV in low-predictive-value scenarios.

Conclusions:

  • QC policy effectiveness is enhanced by considering the specific application context.
  • Common accuracy metrics, with simple assumptions, provide a valuable framework for evaluating QC policy performance.
  • End-user focused metrics improve the practical utility of QC in laboratory settings.