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Developing the Minimum Dataset for the New Mexico Decedent Image Database.

Shamsi Daneshvari Berry^1,2, Philip J Kroth¹, Heather J H Edgar²

¹Western Michigan University, Homer Stryker M.D. School of Medicine, Kalamazoo, Michigan, United States.

Applied Clinical Informatics

|June 2, 2021

Summary

This summary is machine-generated.

A consensus method using experts created an optimal minimum dataset (MDS) for the New Mexico Decedent Image Database (NMDID) full body computed tomography scans, enhancing data longevity.

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Area of Science:

Medical Imaging
Data Science
Forensic Science

Background:

Minimum datasets (MDS) are crucial for organizing research data.
Current methods for MDS determination include ad hoc approaches, expert consultation, or consensus methods.
Ad hoc determination by investigators is the most common approach.

Purpose of the Study:

To determine an optimal minimum dataset (MDS) for full body computed tomography scans using a consensus method.
To apply a modified Delphi method to the New Mexico Decedent Image Database (NMDID).

Main Methods:

Initiated consensus with 50 suggested variables for expert review.
Recruited international experts from diverse scientific disciplines.
Conducted three rounds of variable selection, achieving high consensus.

Main Results:

A total of 59 variables were selected for the MDS.
Only 52% of the selected variables were initially proposed by the resource authors.
A second group of experts validated the selected variables, with no deletions recommended.

Conclusions:

The consensus-driven MDS for the NMDID is more future-proof than datasets determined by single experts or small teams.
This approach leverages global expertise for robust metadata creation.
Ensures greater long-term utility and relevance of the NMDID.