Consensus error grid
Consensus Error Grid (CEG) is a statistical analysis tool used primarily in the field of diabetes management and glucose monitoring. It was developed to assess the clinical accuracy of glucose meters and continuous glucose monitoring systems. The grid is divided into several zones, each representing a different level of potential clinical risk to the patient.
History[edit | edit source]
The Consensus Error Grid was first introduced in 2000 by a group of experts in the field of diabetes care. It was developed as an improvement over the Clarke Error Grid, which was the standard tool for assessing glucose meter accuracy at the time. The Consensus Error Grid was designed to be more clinically relevant and to provide a more accurate representation of the potential risks associated with glucose meter errors.
Structure and Function[edit | edit source]
The Consensus Error Grid is a two-dimensional plot with the reference glucose value on the x-axis and the measured glucose value on the y-axis. The grid is divided into five zones (A, B, C, D, and E), each representing a different level of potential clinical risk:
- Zone A: Values within this zone are considered clinically accurate. This means that the measured glucose value is close enough to the reference value that it would not lead to inappropriate treatment decisions.
- Zone B: Values in this zone are not as accurate as those in Zone A, but they are still close enough to the reference value that they would not lead to serious treatment errors.
- Zone C: Values in this zone could lead to unnecessary treatment interventions.
- Zone D: Values in this zone could lead to significant treatment errors.
- Zone E: Values in this zone could lead to dangerous treatment errors.
The goal of any glucose monitoring system is to have as many values as possible fall within Zones A and B.
Applications[edit | edit source]
The Consensus Error Grid is used by researchers, clinicians, and regulatory bodies to assess the clinical accuracy of glucose monitoring systems. It is also used in the development and testing of new glucose monitoring technologies.
See Also[edit | edit source]
References[edit | edit source]
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