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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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Current Trends in Nursing II01:30

Current Trends in Nursing II

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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
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Interval Level of Measurement00:55

Interval Level of Measurement

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For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Using Generative Art to Convey Past and Future Climate Transitions
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Reference intervals: past, present, and future.

Kelly Doyle1, Dustin R Bunch2

  • 1Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT, USA.

Critical Reviews in Clinical Laboratory Sciences
|April 10, 2023
PubMed
Summary
This summary is machine-generated.

Reference intervals provide essential context for laboratory test results. Novel indirect and individual-based methods offer more accurate and personalized interpretations than traditional population-based approaches.

Keywords:
Reference intervalslaboratory medicinestatistical methods

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Area of Science:

  • Clinical laboratory science
  • Medical diagnostics
  • Biostatistics

Background:

  • Clinical laboratory test results require reference intervals for interpretation in diagnosis, treatment, and monitoring.
  • Limitations in conventional direct methods for establishing reference intervals due to resources and demographics necessitate alternative approaches.
  • Global scientific societies are promoting standardized reference interval best practices through guidelines and collaborations.

Approach:

  • Reviewing traditional direct methods for reference interval establishment and their challenges.
  • Exploring novel indirect methods using computational approaches on mixed datasets.
  • Discussing the development of individual-based and continuous reference intervals.

Key Points:

  • Indirect methods leverage computational techniques to derive reference intervals from complex patient data.
  • Individual-based reference intervals enhance the detection of clinically significant analyte changes compared to population-based intervals.
  • Continuous reference intervals offer improved accuracy over age-based partitions, especially for individuals at partition boundaries.

Conclusions:

  • Advancements in non-conventional reference interval studies are crucial for improving diagnostic accuracy and personalized medicine.
  • Novel methods like indirect and individual-based reference intervals address limitations of traditional approaches.
  • Accurate reference intervals are vital for effective healthcare decision-making.