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GAL-1A Plus Blood Glucose Monitoring System: GAL-1A Plus Blood Glucose Monitoring System is comprised of the GAL-1A Plus Blood Glucose Meter, the GAL-1A Blood Glucose Test Strips. The GAL-1A Plus Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, forearm, or palm. Alternative site testing should be performed only during steady-state (when glucose is not changing rapidly). Testing is done outside the body (In Vitro diagnostic use). It is indicated for lay use by people with diabetes, as an aid to monitoring levels in Diabetes Mellitus and should only be used by a single patient and it should not be shared. It is not indicated for the diagnosis or screening of diabetes or for neonatal use.

The GAL-1A Plus blood glucose monitoring system consists of the GAL-1A Plus meter and GAL-1A Test Strips. It is used for testing of blood glucose by self-testers at home.

This document describes a 510(k) premarket notification for the GAL-1A Plus Blood Glucose Monitoring System, which does not contain an AI/ML component. Therefore, the questions related to AI/ML specific criteria like "number of experts used to establish ground truth," "adjudication method," "MRMC comparative effectiveness study," "standalone performance," and "sample size for the training set" are not applicable.

Here's the available information based on the provided text:

1. Table of acceptance criteria and the reported device performance

The provided text focuses on the substantial equivalence determination and does not detail specific quantitative acceptance criteria or corresponding performance data for the GAL-1A Plus Blood Glucose Monitoring System beyond the disinfectant performance. It states that "Disinfection performance (robustness of meter to multiple cleanings and disinfections) was conducted. Results demonstrate substantial equivalence to the predicate system." This implies the performance met the criteria for substantial equivalence regarding disinfection, but the numerical criteria and results are not provided.

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

The document explicitly states: "No clinical testing was conducted." Therefore, there is no test set for clinical performance and no data provenance information for such a set. For the non-clinical disinfection performance, the sample size and data provenance are not specified.

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

Not applicable, as no clinical testing was conducted and the device does not have an AI/ML component.

4. Adjudication method for the test set

Not applicable, as no clinical testing was conducted and the device does not have an AI/ML component.

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

Not applicable, as the device does not have an AI/ML component.

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

Not applicable, as the device does not have an AI/ML component.

7. The type of ground truth used

For the non-clinical disinfection performance, the "ground truth" would likely be a pre-defined standard or method for evaluating the robustness to cleaning and disinfection, which is not detailed but assumed to be met for substantial equivalence. For clinical performance, no ground truth was established as no clinical testing was performed.

8. The sample size for the training set

Not applicable, as no AI/ML component is mentioned or implied.

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

Not applicable, as no AI/ML component is mentioned or implied.

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The BGM009 Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, forearm, or palm. Alternative site testing should be performed only during steady-state (when glucose is not changing rapidly). The device includes speaking functions but is not intended for use in visually impaired users. It is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid in monitoring the effectiveness of diabetes control and should only be used by a single patient and it should not be shared. It is not indicated for the diagnosis or screening of diabetes or for neonatal use.

The BGM009 Blood Glucose Test Strips are to be used with the BGM009 Blood Glucose Meter to quantitatively measure glucose in capillary whole blood taken from fingertips, palm, or forearm.

The BGM009 blood glucose meter and BGM009 test strips are used for testing of blood glucose.

The provided text focuses on the FDA 510(k) premarket notification for the BGM009 Blood Glucose Monitoring System. It describes the device's intended use, classification, and comparison to a predicate device. While it mentions "clinical testing" and "accuracy study," it does not provide specific acceptance criteria or detailed results of a study proving the device meets those criteria.

Therefore, many of the requested details about acceptance criteria, specific performance metrics, sample sizes, ground truth establishment, expert qualifications, and multi-reader studies are not available in the provided document.

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

1. Table of Acceptance Criteria and Reported Device Performance

This information is not provided in the document. The document states "Results demonstrate substantial equivalence to the predicate system" for non-clinical and clinical testing, but it does not specify the quantitative acceptance criteria (e.g., accuracy percentages within certain glucose ranges) or the numerical performance results against those criteria.

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

• Test Set Sample Size: The document mentions "an accuracy study was conducted with home users" and "disinfection testing with recommended disinfectant wipes was done using an animal virus test model," but it does not specify the sample sizes for either.
• Data Provenance: The study on home users is implied to be prospective (a clinical study). The location of the home users (country) is not specified.

3. Number of Experts Used to Establish Ground Truth and Qualifications

This information is not provided. The document does not describe how the "ground truth" (reference glucose values) was established for the accuracy study.

4. Adjudication Method

This information is not provided. This concept is typically more relevant for subjective diagnoses from image-based medical devices, which is not the case for a blood glucose monitor.

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

This information is not provided and is not applicable to a blood glucose monitoring system, which provides a numerical output rather than an interpretation requiring multiple readers. The "accuracy study" mentioned is likely comparing device readings to a reference method, not human interpretations.

6. Standalone Performance Study

Yes, a standalone study (algorithm only without human-in-the-loop performance) was effectively done. The "accuracy study" and "precision testing" described for the blood glucose monitoring system are inherently standalone performance evaluations of the device itself against a reference method.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for the accuracy study. For blood glucose monitors, the ground truth is typically established by laboratory reference methods (e.g., YSI glucose analyzer) performed on the same blood samples. This is a common practice in such studies, but not explicitly stated here.

8. Sample Size for the Training Set

This information is not provided. The document makes no mention of a "training set" as it would for a machine learning model. For a device like a blood glucose monitor, the "training" typically refers to the device's calibration and algorithm development, which would use internal data, not a formally defined "training set" in the context of FDA submissions for machine learning.

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

This information is not provided and is not applicable in the same way it would be for AI/ML devices.

In summary, the provided text provides high-level information about the regulatory submission of a blood glucose monitoring system, but it lacks the detailed technical and clinical study results that would allow for a comprehensive answer to the requested questions regarding specific acceptance criteria and study data.

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