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The On Call® Vivid Pal Blood Glucose Monitoring System is an electrochemical enzymatic assay for the quantitative detection of glucose in fresh capillary whole blood from the fingertip by people with diabetes at home as an aid in monitoring the effectiveness of diabetes control programs. Alternate (forearm and palm) testing sites should be used only when blood glucose level is not changing rapidly. The On Call® Vivid Blood Pal Glucose Monitoring System is intended to be used by a single patient and should not be shared. It is for in vitro diagnostic use only.

The On Call Vivid Blood Pal Glucose Monitoring System should not be used for the diagnosis of or screening for diabetes mellitus, or use on neonates.

The On Call Vivid Pal Blood Glucose Test Strips are used with the On Call Vivid Blood Glucose Meter in the quantitative measurement of glucose in capillary blood from the fingertip, forearm and palm.

The On Call Vivid Pal Blood Glucose Control Solution is for use with the On Call Vivid Pal Blood Glucose Meter and Strips as a quality control check to verify that the meter and test strips are working together properly and that the test is performing correctly.

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The provided text is a 510(k) summary for the On Call® Vivid Pal Blood Glucose Monitoring System, which does not contain the detailed study information required to answer all aspects of your request. This document is a regulatory approval notice and focuses on the substantial equivalence determination rather than a comprehensive report of validation studies.

However, based on the information available and general knowledge of blood glucose monitoring system approvals, I can provide the following:

Device Name: On Call® Vivid Pal Blood Glucose Monitoring System

Indications for Use: The On Call® Vivid Pal Blood Glucose Monitoring System is an electrochemical enzymatic assay for the quantitative detection of glucose in fresh capillary whole blood from the fingertip by people with diabetes at home as an aid in monitoring the effectiveness of diabetes control programs. Alternate (forearm and palm) testing sites should be used only when blood glucose level is not changing rapidly. The device is intended for single patient use and is for in vitro diagnostic use only. It should not be used for the diagnosis of or screening for diabetes mellitus, or use on neonates.

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

The provided document does not contain a specific table detailing acceptance criteria and reported device performance. For blood glucose monitoring systems, acceptance criteria typically follow ISO 15197 standards (or similar regulatory guidelines) which specify accuracy requirements compared to a laboratory reference method. These usually involve:

• A certain percentage of results falling within ±X mg/dL (or mmol/L) of the reference for glucose concentrations below Y mg/dL (or mmol/L).
• A certain percentage of results falling within ±Z% of the reference for glucose concentrations above Y mg/dL (or mmol/L).

Without the study report, the specific numerical criteria and the device's performance against them cannot be extracted from this document.

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

This information is not available in the provided 510(k) letter. The letter is a communication of regulatory clearance, not a clinical study report. Such details would typically be found in the manufacturer's submission or detailed study reports.

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 question is not applicable to a blood glucose monitoring system. The "ground truth" for glucose measurements is established by a highly accurate laboratory reference method (e.g., YSI analyzer) performed by trained lab personnel, not by medical experts in an interpretive role like radiologists.

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

This question is not applicable to a blood glucose monitoring system. Adjudication typically refers to resolving discrepancies between multiple human readers or interpretations, which is not how glucose measurements are validated.

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

This question is not applicable to a blood glucose monitoring system. This type of study design is relevant for AI-powered diagnostic imaging devices where human readers interpret images. A blood glucose monitor is a standalone measurement device.

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

Yes, a blood glucose monitoring system inherently performs as a standalone device. Its performance is evaluated by directly comparing its glucose readings to those obtained from a laboratory reference method. There is no "human-in-the-loop" interpretation step that would benefit from, or be hindered by, an AI algorithm in the context of reading the glucose value. The device's algorithm processes the electrochemical signal to produce a glucose concentration.

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

For a blood glucose monitoring system, the ground truth is established by a highly accurate laboratory reference method, such as a YSI glucose analyzer, typically using venous blood samples.

8. The sample size for the training set

This information is not available in the provided 510(k) letter. Blood glucose meters do not typically have "training sets" in the same way machine learning algorithms do. Their calibration is established during manufacturing and validated through accuracy studies. While there might be internal data used to develop and refine the electrochemical enzymatic assay and its signal processing, it's not usually referred to as a "training set" in the context of regulatory submissions for these devices.

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

As noted above, the concept of a "training set" and associated ground truth establishment for a blood glucose meter in the context of an AI/ML algorithm is not directly applicable. If any internal development involved data-driven optimization, the ground truth would still have been established using a laboratory reference method for glucose concentration.

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