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The FORA G71a Blood Glucose Monitoring System/TD-4274 Blood Glucose Monitoring System are intended for use in the quantitative measurement of glucose in fresh capillary whole blood from the finger and the following alternative sites: the palm, the forearm, the upper-arm, the calf and the thigh. It is intended for use by healthcare professionals and people with diabetes mellitus at home as an aid in monitoring the effectiveness of diabetes control program. It is not intended for the diagnosis of or screening for diabetes mellitus, and is not intended for use on neonates.

The alternative site testing in the FORA G71a Blood Glucose Monitoring System /TD-4274 Blood Glucose Monitoring System can be used only during steady-state blood glucose conditions.

The system consists of three main products: the meter, test strips, and control solutions. These products have been designed, tested, and proven to work together as a system to produce accurate blood glucose test results. Use only FORA G71a / TD-4274 test strips and control solutions with the FORA G71a Blood Glucose Monitoring System / TD-4274 Blood Glucose Monitoring System.

Here's an analysis of the provided text regarding the acceptance criteria and study for the FORA G71a / TD-4274 Blood Glucose Monitoring System:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state specific acceptance criteria or detailed performance metrics. Instead, it relies on demonstrating substantial equivalence to a predicate device. The key performance characteristic mentioned is "system accuracy."

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

The document does not specify the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It simply states that "A comparison of system accuracy performance demonstrated..." without providing details on the study design.

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

The document does not mention the use of experts to establish ground truth. For blood glucose monitoring systems, ground truth is typically established by laboratory reference methods (e.g., YSI analyzer), rather than expert consensus on images or interpretations.

4. Adjudication Method for the Test Set

As experts are not mentioned in relation to ground truth, an adjudication method is not applicable in this context.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not performed or reported. This type of study is typically used for imaging interpretation devices where human readers' performance is being evaluated with and without AI assistance. A blood glucose monitor is a standalone diagnostic device.

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

Yes, a standalone performance study in the form of a "system accuracy performance" comparison was conducted. This type of device inherently operates in a "standalone" manner, providing a direct measurement without human interpretation beyond reading the displayed result. The comparison was to a predicate device, implying that the new device's readings were directly compared to those of the predicate device.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used, but for blood glucose monitoring systems, the ground truth is overwhelmingly laboratory reference methods (e.g., YSI glucose analyzer) on blood samples. The term "system accuracy performance" implies a comparison to such a reference method, or at least a comparison to the predicate device which would have been validated against a reference method.

8. Sample Size for the Training Set

The document does not mention a training set or its sample size. Glucose meters are typically developed and validated using a series of experiments and comparisons, not usually via a machine learning "training set" in the conventional sense of AI. The "modification in the software of the glucose meter" would have undergone internal development and testing, but not typically a labeled training data set as seen in AI/ML applications.

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

As no training set is described, this information is not applicable and not provided.

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