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CADORmed: a tool for internal dose assessment
Bernard Landry1, Bastian Breustedt2, Carlo Maria Castellani3
1EDF CNPE du Blayais, 33820 Braud et Saint Louis, France.
CADORmed is a free Excel tool for effective dose assessment in special monitoring, utilizing ICRP dose coefficients and EURADOS guidelines. It handles complex intake scenarios and data below detection limits for accurate committed dose calculations.
Area of Science:
- Medical Physics
- Radiation Protection
- Computational Toxicology
Background:
- Effective dose assessment is crucial for radiation protection.
- Existing tools may not incorporate the latest ICRP dose coefficients or handle specific monitoring scenarios.
- Standardized methodologies are needed for reliable dose calculations.
Purpose of the Study:
- To introduce CADORmed, a free Excel tool for committed effective dose assessment.
- To provide a tool for special monitoring applications using updated ICRP OIR dose coefficients.
- To implement calculations according to EURADOS guidelines and incorporate advanced data handling techniques.
Main Methods:
- Bespoke Excel tool development.
- Application of ICRP OIR dose coefficients.
- EURADOS 2013-1 guidelines for calculations.
- Chi-squared goodness of fit test with scattering factors.
- Maximum likelihood method for intake calculation.
- Inclusion of data below detection limits using allocated values.
- Advanced options for mixed exposures and unknown intake dates.
Main Results:
- CADORmed is validated and available for special monitoring.
- The tool accurately calculates committed effective dose using current standards.
- It facilitates the identification of rogue data and incorporates advanced exposure scenarios.
- Measurements below detection limits are handled appropriately.
Conclusions:
- CADORmed provides a robust and flexible solution for committed effective dose assessment in special monitoring.
- The tool adheres to established EURADOS guidelines and incorporates advanced statistical methods.
- Its features support accurate dose assessment even with challenging data, including values below detection limits.
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