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Related Concept Videos

Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Data Validation01:15

Data Validation

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
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Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

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A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

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A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
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Updated: Jan 23, 2026

Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy
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Development and Validation of an Endometrial Cancer Algorithm in US Claims Data.

Kimberly Daniels1, Cachet Wenziger1, Sampada Gandhi2

  • 1Carelon Research, Wilmington, Delaware, USA.

Pharmacoepidemiology and Drug Safety
|January 21, 2026
PubMed
Summary
This summary is machine-generated.

This study developed accurate algorithms to identify endometrial cancer cases using ICD-9-CM and ICD-10-CM codes. The algorithms demonstrated high positive predictive value and sensitivity, with minimal false positives for endometrial cancer detection.

Keywords:
endometrial cancermenopausepositive predictive valuesensitivityvaginal estrogenvalidation

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Area of Science:

  • Oncology
  • Medical Informatics
  • Epidemiology

Background:

  • Accurate identification of endometrial cancer cases is crucial for epidemiological studies and post-authorization safety assessments.
  • Existing coding systems require validation for precise case ascertainment in large datasets.

Purpose of the Study:

  • To develop and validate algorithms for identifying endometrial cancer incidence using ICD-9-CM and ICD-10-CM codes.
  • To support a post-authorization safety study on hormone therapies by ensuring reliable case identification.

Main Methods:

  • Utilized national claims data from the HealthCare Integrated Research Database (HIRD).
  • Developed screening algorithms based on diagnosis codes and adjudicated by expert review.
  • Calculated positive predictive value (PPV) and conditional sensitivity for algorithm performance.

Main Results:

  • An algorithm using two ICD-9-CM codes (182.0 or 182.8) achieved a PPV of 91.2% and sensitivity of 99.3%.
  • An algorithm using two ICD-10-CM codes (C54.1, C54.8, or C54.9) achieved a PPV of 97.0% and sensitivity of 99.5%.
  • Both algorithms demonstrated PPVs and sensitivities exceeding 75% across all test cohorts.

Conclusions:

  • Two diagnosis codes for endometrial cancer accurately identify confirmed cases in both ICD-9-CM and ICD-10-CM systems.
  • The developed algorithms exhibit high accuracy and minimal false positives for endometrial cancer.
  • The validated algorithms are suitable for use in large-scale epidemiological and safety studies.