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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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Measuring Reaction Rates03:09

Measuring Reaction Rates

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Polarimetry finds application in chemical kinetics to measure the concentration and reaction kinetics of optically active substances during a chemical reaction. Optically active substances have the capability of rotating the plane of polarization of linearly polarized light passing through them—a feature called optical rotation. Optical activity is attributed to the molecular structure of substances. Normal monochromatic light is unpolarized and possesses oscillations of the electrical...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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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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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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The Impact of Breast Density Notification on Interval Cancer Rates.

Jennifer Stone1, Ross Marriott1, Marcela Orellana2

  • 1School of Population and Global Health, University of Western Australia, Perth, Western Australia, Australia.

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PubMed
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Women notified of dense breasts have higher interval breast cancer detection rates, especially within the first year post-screening. This suggests a need to refine screening program metrics and could enable earlier cancer diagnoses.

Keywords:
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Area of Science:

  • Radiology and Imaging
  • Oncology
  • Public Health

Background:

  • Increased breast density is a known risk factor for breast cancer.
  • Interval-detected cancers, diagnosed between screenings, are a key concern.
  • This study examines interval cancer detection rates based on breast density notification and diagnosis timing.

Purpose of the Study:

  • To compare interval cancer detection rates in women notified of dense breasts versus those not notified.
  • To analyze detection rates based on the timing of diagnosis (0-12 months vs. 13-24 months post-screening).

Main Methods:

  • Analysis of 401,254 screening records from 235,333 women in Western Australia (2016-2019).
  • Calculation of crude and age-standardized interval cancer rates (ASR) per 10,000 client-years.
  • Stratification of rates by breast density notification status, screening round, and diagnosis timing.

Main Results:

  • Interval cancer rates were 2-6 times higher for women notified of dense breasts.
  • Age-standardized rates were higher in the first year (0-12 months) compared to the second year (13-24 months) post-screening for notified women.
  • This trend was particularly pronounced in first-time screeners and persisted across different screening rounds.

Conclusions:

  • Interval cancer rates are similar or higher within 12 months post-screening for women notified of dense breasts, challenging national findings.
  • Screening program performance metrics, particularly interval cancer rate targets, may require adjustment for density notification programs.
  • Breast density notification may facilitate earlier detection of interval-cancers.