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Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

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Related Experiment Video

Updated: Jun 8, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Using SNOMED CT to identify a crossmap between two classification systems: a comparison with an expert-based and a

Ferishta Bakhshi-Raiez1, Ronald Cornet, Rob J Bosman

  • 1Department of Medical Informatics, Academic Medical center, Universiteit van Amsterdam, The Netherlands. f.raiez@amc.uva.nl

Studies in Health Technology and Informatics
|September 16, 2010
PubMed
Summary

Using SNOMED CT as an intermediary reference terminology helps crossmap successive versions of health care classification systems. This approach ensures documentation continuity without significantly impacting patient outcomes.

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Area of Science:

  • Health Informatics
  • Medical Classification Systems
  • Clinical Documentation

Background:

  • Maintaining continuity in health care documentation across evolving classification systems is crucial.
  • Reference terminologies can facilitate crossmapping between different versions of these systems.

Purpose of the Study:

  • To evaluate SNOMED CT as an intermediary for creating crossmaps between intensive care classification system versions.
  • To compare SNOMED CT-generated crossmaps with expert-based and data-driven methods.
  • To assess the impact of different crossmapping strategies on health care outcomes.

Main Methods:

  • Utilized SNOMED CT to generate crossmaps between two versions of an intensive care classification system.
  • Compared SNOMED CT crossmaps against expert-based and data-driven crossmaps.
  • Evaluated the influence of these crossmapping strategies on health care outcomes.

Main Results:

  • SNOMED CT crossmaps agreed with expert-based and data-driven methods in 50% of cases.
  • An overlap was observed between SNOMED CT crossmaps and the other two strategies in differing cases.
  • No significant influence of crossmap differences on health care outcomes was detected.

Conclusions:

  • SNOMED CT effectively serves as an intermediary for crossmapping between classification system versions.
  • The use of SNOMED CT supports the continuity of health care documentation.
  • Crossmapping strategies, including SNOMED CT, do not significantly alter health care outcomes.