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Supporting prioritization efforts of higher-order reference providers using evidence from the Joint Committee for Traceability in Laboratory Medicine database

  • Mauro Panteghini ORCID logo EMAIL logo and Robert Wielgosz
Published/Copyright: April 11, 2025

Abstract

The Joint Committee for Traceability in Laboratory Medicine (JCTLM) database represents a valuable resource for implementing metrological traceability in laboratory medicine. Three main database users can be identified: (a) in vitro diagnostic (IVD) manufacturers, using the database information for meeting ISO 17511:2020 requirements, (b) laboratory professionals, for defining the quality of their test results, and (c) providers of higher-order certified reference materials (CRM) and reference measurement procedures (RMP), to be helped in improving the suitability of their products, if needed, and assistance with prioritizing their future efforts. In this report, we focus on the utility of the information provided (or still not provided) by the JCTLM database on this last category of users. Two types of information are discussed: (a) the use of listed CRMs as common calibrators intended to transfer trueness from the top of the calibration hierarchy to commercial IVD calibrators, and (b) the measurement uncertainty (MU) of CRM certified values and the reproducibility characteristics of RMP measurements, considering their impact on the MU of clinical samples, when compared to maximum allowable MU (MAU). The discussion output is a recommendation for suppliers to respond urgently to the need to provide higher-order references (CRMs and/or RMPs) for a number of key analytes that are currently lacking or do not yet fully meet quality criteria related to: (a) commutability assessment, (b) contribution to MAU fulfilment, and (c) demonstration of the extent of equivalence to an already listed higher-order reference.


Corresponding author: Mauro Panteghini, Department of Laboratory Medicine, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 9 Sklodowskiej-Curie Street, 85-094 Bydgoszcz, Torun, Poland, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-03-31
Accepted: 2025-03-31
Published Online: 2025-04-11
Published in Print: 2025-07-28

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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