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Cited by 406

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

Glucose Meters: A Review of Technical Challenges to Obtaining Accurate Results

Open access
Tonyushkina, Ksenia, Nichols, James H.·2009·Journal of Diabetes Science and Technology

Abstract

Glucose meters are universally utilized in the management of hypoglycemic and hyperglycemic disorders in a variety of healthcare settings. Establishing the accuracy of glucose meters, however, is challenging. Glucose meters can only analyze whole blood, and glucose is unstable in whole blood. Technical accuracy is defined as the closeness of agreement between a test result and the true value of that analyte. Truth for glucose is analysis by isotope dilution mass spectrometry, and frozen serum standards analyzed by this method are available from the National Institute of Standards and Technology. Truth for whole blood has not been established, and cells must be separated from the whole blood matrix before analysis by a method like isotope dilution mass spectrometry. Serum cannot be analyzed by glucose meters, and isotope dilution mass spectrometry is not commonly available in most hospitals and diabetes clinics to evaluate glucose meter accuracy. Consensus standards recommend comparing whole blood analysis on a glucose meter against plasma/serum centrifuged from a capillary specimen and analyzed by a clinical laboratory comparative method. Yet capillary samples may not provide sufficient volume to test by both methods, and venous samples may be used as an alternative when differences between venous and capillary blood are considered. There are thus multiple complexities involved in defining technical accuracy and no clear consensus among standards agencies and professional societies on accuracy criteria. Clinicians, however, are more concerned with clinical agreement of the glucose meter with a serum/plasma laboratory result. Acceptance criteria for clinical agreement vary across the range of glucose concentrations and depend on how the result will be used in screening or management of the patient. A variety of factors can affect glucose meter results, including operator technique, environmental exposure, and patient factors, such as medication, oxygen therapy, anemia, hypotension, and other disease states. This article reviews the challenges involved in obtaining accurate glucose meter results.

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Cites

Show all 41 references

Research works printed but not linked: 12

  1. Bernstein RK, 1997, Dr. Bernstein's diabetes solution. A complete guide to achieving normal blood sugars
  2. Gore DC, 2001, J Trauma., 51, 540
  3. ACE/ADA Task Force on Inpatient Diabetes, 2006, Endocr Pract., 12, 4
  4. International Organization for Standardization, 2007, International vocabulary of metrology—Basic and general concepts and associated terms (VIM). ISO/IEC Guide 99.
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  8. Instituto Mexicano del Seguro Social, 1998, Reactive strip assessment protocol for determination of glucose in blood
  9. TNO Center for Medical Technology, 1991, Quality guideline for non-implantable portable blood glucose monitors for self-monitoring
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  12. 10.1213/01.ane.0000264007.46873.0f · doi:10.1213/01.ane.0000264007.46873.0f

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Metadata

Authors
Tonyushkina, K., & Nichols, J. H.
Venue
Journal of Diabetes Science and Technology
Published
2009-07
Volume
3
Issue
4
Pages
971-980
ISSN
1932-2968 (ISSN-L)
OpenAlex
W2112149778
Cited by
406
References
41
FWCI
6.30
Type
journal article
Language
en
Open access
green
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)

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CoChat. (2026). Citation check: Glucose Meters: A Review of Technical Challenges to Obtaining Accurate Results. https://app.cochat.ai/papers/10.1177/193229680900300446
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Tonyushkina, K., & Nichols, J. H. (2009). Glucose Meters: A Review of Technical Challenges to Obtaining Accurate Results. Journal of Diabetes Science and Technology. https://doi.org/10.1177/193229680900300446

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.