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The Gluco Track Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary, fingerstick whole blood. Testing is done outside the body (in vitro diagnostic use). It is indicated for both lay use by people with diabetes and in a clinical setting by health care professionals, as an aid to monitoring levels in Diabetes Mellitus.

Based on an electrochemical biosensor technology and the principle of capillary action, Gluco Track Blood Glucose Monitoring System only needs a small amount of blood. Capillary action at the end of the test strip draws the blood into the action chamber and your blood glucose result is precisely and displayed in 6 seconds.

This document does not provide enough information to construct the requested table and complete all sections of the study description. The provided text is a 510(k) summary for a blood glucose monitoring system, which primarily focuses on establishing substantial equivalence to a predicate device rather than detailing specific performance studies with acceptance criteria and ground truth.

Here's what can be extracted and what information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample Size: Not specified.
• Data Provenance: The document states "Pre-clinical and clinical data are employed upon submission of this 510(K) premarket notification," but does not provide details on the origin (e.g., country) or whether it was retrospective or prospective.

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

• This information is not provided.

4. Adjudication method for the test set

• This information is not provided.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done

• No, an MRMC study is not mentioned as this device is a blood glucose monitoring system, not an imaging or diagnostic AI requiring human reader interaction in that context. The "clinical data" mentioned would likely be compared against a laboratory reference method, not human interpretation.

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

• This device is a standalone algorithm/device for measuring glucose. There isn't a "human-in-the-loop" component in the way AI in imaging is typically discussed. Its performance would inherently be "standalone." However, specific performance metrics against a defined (presumably laboratory-based) ground truth are not provided in this document.

7. The type of ground truth used

• While not explicitly stated for the "clinical data," for blood glucose monitoring systems, the ground truth is typically established by laboratory reference methods (e.g., YSI or hexokinase methods) performed on venous blood samples. The device's results (from capillary blood) are then compared against these reference values.

8. The sample size for the training set

• Not specified. The document does not explicitly mention a "training set" in the context of machine learning, but rather "pre-clinical and clinical data" which would be used to validate the device.

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

• Not specified. Based on the nature of the device, the ground truth for any data used in development and validation would be established by laboratory reference methods for glucose measurement.

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