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The Assure Pro Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control. The Assure Pro Blood Glucose Monitoring System is not intended for the diagnosis of or screening for diabetes mellitus, and is not intended for use on neonates.

The Assure Pro Blood Glucose Monitoring System consists of a meter, test strips, and two levels of control solutions for use as an aid to monitor the effectiveness of diabetes control.

This document describes the Assure Pro Meter Test Strips and its associated system, cleared under K090332.

1. A table of acceptance criteria and the reported device performance

The provided document does not explicitly state numerical acceptance criteria in a table format for the Assure Pro Meter Test Strips. It broadly mentions "Functional and Safety Testing" and "Clinical testing included evaluation of accuracy for the finger stick". Without clear, quantifiable acceptance criteria from the 510(k) summary, specific reported performance against these criteria cannot be detailed.

However, the "Conclusion" states: "Labeling, bench testing results and clinical testing results support the Indications for Use and the claim of substantial equivalence to the predicate." This implies that the device's performance, as evaluated through these tests, was deemed acceptable by the FDA for its intended use.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document mentions "clinical testing included evaluation of accuracy for the finger stick" but does not specify the sample size used for this clinical test set.

The data provenance (country of origin, retrospective/prospective) is also not explicitly stated in the provided text.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the document. For blood glucose monitoring systems, ground truth is typically established by laboratory reference methods, not by human expert interpretation of images.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not provided in the document. Adjudication methods are typically relevant for studies involving human interpretation, not direct quantitative measurements like blood glucose.

5. 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 MRMC study was mentioned. This type of study is not applicable as the device is a blood glucose monitoring system, not an AI-powered diagnostic imaging tool requiring human reader interpretation.

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

The device is a standalone system – a blood glucose meter with test strips. Its performance ("accuracy for the finger stick") would inherently be evaluated without human interpretation of the measurement itself. The human-in-the-loop is the user performing the test and reading the numerical result.

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

The document does not explicitly state the specific type of ground truth used, but for blood glucose monitoring systems, the ground truth is typically established by laboratory reference methods for glucose measurement (e.g., from a central lab or a highly accurate analyzer) against which the device's readings are compared.

8. The sample size for the training set

The device is a traditional medical device (blood glucose meter and test strips), not an AI/machine learning algorithm that requires a "training set" in the computational sense. Therefore, this concept does not apply, and no training set size is mentioned.

9. How the ground truth for the training set was established

As the device is not an AI/machine learning algorithm with a training set, this question is not applicable.

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