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The GLUCOMETER® DEX® 2 Blood Glucose Meter and the GLUCOMETER® DEX® Test Sensors are used to measure the glucose levels in whole blood from specimens taken from the fingers or an alternate puncture sites within cartain conditions. The GLUCOMETER® DEX® Diabetes Care System is an over-the-counter (OTC) device used by persons with diabetes in a home setting and by healthcare professionals in healthcare facilities. The frequent monitoring of blood glucose is an adjunct to the care of persons with diabetes.

The GLUCOMETER® DEX® Diabetes Care System includes: GLUCOMETER® DEX® 2 Blood Glucose Meter GLUCOMTER® DEX® Test Sensors This Diabetes Care System consists of an electrochemical methodbased meter and dry reagent sensor (test strips) designed for testing blood glucose by persons with diabetes or by healthcare professionals in the home or in healthcare facilities.

The provided text describes the GLUCOMETER® DEX® Diabetes Care System, an electrochemical method-based meter and dry reagent sensor (test strips) designed for testing blood glucose. The submission states that the device is conceptually the same as other blood glucose monitoring products. The acceptance criteria and the study conducted are described as follows:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not explicitly stated, but the summary mentions "an evaluation... conducted in a clinical setting by persons with diabetes." It does not provide specific numbers for participants or tests performed.
• Data Provenance: The study was conducted in a "clinical setting." No specific country of origin is mentioned, but the submitter is based in Elkhart, IN, USA. The study appears to be a prospective clinical evaluation as it involves actual users, "persons with diabetes," and healthcare professionals using the device in a clinical setting.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided in the summary. The "ground truth" seems to be established by comparison with "blood glucose results from fingersticks," implying a reference method, but the qualifications of individuals performing or interpreting these reference tests are not detailed.

4. Adjudication Method for the Test Set

This information is not provided in the summary.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study is mentioned. The study described focuses on the equivalence of alternate puncture site results to fingerstick results, rather than comparing human reader performance with and without AI assistance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The device is a blood glucose meter system used by individuals with diabetes or healthcare professionals. Therefore, its performance intrinsically involves "human-in-the-loop." A standalone algorithm-only performance study, as typically understood for imaging or diagnostic algorithms, is not applicable or described for this type of device. The "performance" refers to the accuracy of the device itself in measuring glucose.

7. Type of Ground Truth Used

The ground truth used for comparison was "blood glucose results from fingersticks." This implies that fingerstick blood glucose measurements, likely obtained using a recognized reference method or another validated blood glucose meter, served as the standard against which alternate puncture site measurements were compared.

8. Sample Size for the Training Set

This information is not applicable. The GLUCOMETER® DEX® Diabetes Care System is described as an "electrochemical method-based meter" employing an "amperometric glucose oxidase method." This is a biochemical measurement system, not a machine learning or AI-driven device that requires a training set in the conventional sense.

9. How the Ground Truth for the Training Set Was Established

This information is not applicable, as there is no mention of a training set for an AI/machine learning model. The device's underlying technology is based on electrochemical principles.

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The GLUCOMETER ELITE® and the GLUCOMETER ELITE® XL Blood Glucose Meters are used with GLUCOMETER ELITE® Test Strips to measure glucose levels in whole blood from specimens taken from the fingers or an alternate puncture site within certain conditions. The GLUCOMETER ELITE Diabetes Care System is an overthe-counter (OTC) device used by persons with diabetes in a home setting and by healthcare professionals in healthcare facilities. The frequent monitoring of blood glucose is an adjunct to the care of persons with diabetes.

The GLUCOMETER ELITE® Diabetes Care System includes the GLUCOMETER ELITE®, GLUCOMETER ELITE® XL Blood Glucose Meters and the GLUCOMETER ELITE® Test Strips. The system is an electrochemical method-based meter and dry reagent sensor (test strips) designed for testing blood glucose by persons with diabetes in the home or by healthcare professionals in healthcare facilities.

The provided text describes the GLUCOMETER ELITE® Diabetes Care System. However, it does not contain specific acceptance criteria (like accuracy percentages or specific ranges) or detailed study outcomes that would fit into a table format as requested. The document is a 510(k) summary focused on demonstrating substantial equivalence to predicate devices, rather than a detailed clinical trial report with specific performance metrics.

Therefore, I cannot provide a table of acceptance criteria and reported device performance with specific numerical values for metrics like accuracy, sensitivity, or specificity.

I can, however, extract the information that is present:

Acceptance Criteria and Reported Device Performance:

Here's what can be extracted regarding the study and ground truth, along with limitations due to the nature of the provided document:

1. Sample size used for the test set and the data provenance:

   • Sample Size: Not specified in the provided text.
   • Data Provenance: The study was conducted "in a clinical setting by persons with diabetes." No country of origin is explicitly mentioned, but the submitter is Bayer Diagnostics in Elkhart, IN, USA, and the FDA approval is for the US market. It's not explicitly stated if it was retrospective or prospective, but clinical studies for device approval are typically prospective.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Number of Experts: Not specified.
   • Qualifications of Experts: Not specified.

3. Adjudication method for the test set:

   • Not specified.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No, an MRMC comparative effectiveness study was not done. This device is a blood glucose meter, not an AI-powered diagnostic imaging tool that would involve human readers.

5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:

   • The study assessed the device's ability to produce blood glucose results from alternate puncture sites, compared to fingersticks. This is a "standalone" performance assessment in the sense that it's evaluating the device's measurement capability directly, not its interaction with an interpreting human, beyond the user operating the device.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • The "ground truth" for comparison was blood glucose results obtained from fingersticks. This implies that fingerstick measurements (likely taken with a reference method or a predicate device) were considered the standard against which alternate puncture site measurements were compared.

7. The sample size for the training set:

   • Not specified. This document describes a clinical performance assessment, not the development and training of a machine learning model. Therefore, the concept of a "training set" in the AI sense does not apply here.

8. How the ground truth for the training set was established:

   • Not applicable, as no AI training set is mentioned.

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