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Related Concept Videos

Introduction to z Scores01:06

Introduction to z Scores

11.2K
A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
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Introduction to z Scores01:05

Introduction to z Scores

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A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

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z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Calculating the Equilibrium Constant02:46

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The equilibrium constant for a reaction is calculated from the equilibrium concentrations (or pressures) of its reactants and products. If these concentrations are known, the calculation simply involves their substitution into the Kc expression.
For example, gaseous nitrogen dioxide forms dinitrogen tetroxide according to this equation:
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Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

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The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
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Related Experiment Video

Updated: Feb 4, 2026

A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS
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Calculated decisions: qSOFA (quick SOFA) score for sepsis

Kamal Medlej1

  • 1Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA

Emergency Medicine Practice
|October 4, 2018
PubMed
Summary
This summary is machine-generated.

The quick Sequential Organ Failure Assessment (qSOFA) score helps identify patients with suspected infections who face a high risk of death during hospitalization. This tool is crucial for patients outside the intensive care unit.

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Area of Science:

  • Critical Care Medicine
  • Infectious Diseases
  • Clinical Epidemiology

Background:

  • Sepsis and severe infections pose significant global health challenges.
  • Accurate risk stratification is essential for timely and effective patient management.
  • Existing scoring systems may have limitations in non-intensive care unit settings.

Purpose of the Study:

  • To evaluate the utility of the quick Sequential Organ Failure Assessment (qSOFA) score.
  • To identify patients with suspected infection at high risk for in-hospital mortality.
  • To assess the score's applicability in settings outside the intensive care unit.

Main Methods:

  • Retrospective analysis of patient data.
  • Inclusion of patients with suspected infections.
  • Calculation and assessment of qSOFA scores.
  • Correlation of qSOFA scores with in-hospital mortality outcomes.

Main Results:

  • The qSOFA score effectively identified a subset of patients with suspected infection at elevated risk of mortality.
  • High qSOFA scores were significantly associated with increased in-hospital death rates.
  • The score demonstrated utility in predicting mortality among patients managed outside the ICU.

Conclusions:

  • The qSOFA score is a valuable, simple bedside tool for early risk identification in patients with suspected infections.
  • Implementation of qSOFA can aid clinicians in resource allocation and prompt intervention for high-risk individuals.
  • Further validation in diverse clinical settings is warranted to optimize its use in critical care triage.