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The Q.STEPS Biometer G/C Dual Monitoring System is intended for use with Q.STEPS Glucose and Cholesterol Test Strips with Q.STEPS Biometer G/C System by healthcare professionals and home users. Q.STEPS Biometer G/C System provides a quantitative measurement of Glucose and Cholesterol in whole blood from the fingertips. The Glucose measurements are used in helping the management of carbohydrate metabolism disorders including diabetes mellitus, idiopathic hypoglycemia and pancreatic islet cell tumors. Cholesterol measurements are used in the management of disorders involving excess cholesterol in the blood, lipid and lipoprotein metabolism disorders.

The Q. STEPS™ Biometer G/C Dual Monitoring System uses enzymatic electrochemical biosensor technology for a quick and easy measurement of the whole blood glucose and cholesterol levels. When finger stick blood is applied to the test spot of the biosensor (test strip), a reduction oxidation reaction of D-Glucose or Cholesterol occurs, which is catalyzed by Glucose Oxidase and Cholesterol Oxidase respectively, and therefore, the magnitude of the electron transfer at the electrode is proportional to the glucose or cholesterol concentration in the blood. The monitor will quantify the glucose and the cholesterol levels in the blood, and then display on the readout of the monitor.

The provided 510(k) summary for the Q.STEPS™ Biometer G/C Dual Monitoring System does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and the comprehensive study that proves the device meets those criteria. The summary focuses primarily on device description, classification, predicate devices, and intended use, rather than a detailed performance study report.

However, based on the available information regarding the device's function (measuring glucose and cholesterol), and common practices for such devices, we can infer some general acceptance criteria and discuss the provided information.

Here's an attempt to structure the answer based on the prompt and the limited information:

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

The provided document does not explicitly state specific acceptance criteria in terms of numerical accuracy targets (e.g., +/- 15% for glucose, specific bias, precision values). It also does not report detailed device performance data from a specific study.

For a glucose and cholesterol meter, typical acceptance criteria, often outlined in standards like ISO 15197 for blood glucose monitoring systems, would include:

Note: The 510(k) summary typically includes a performance section with such data, but it is missing from the provided extract. The approval (K050182) indicates that such data were reviewed by the FDA, but they are not included in this summary.

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 provided in the given 510(k) summary. A typical submission would include details about clinical study sample sizes, patient demographics, and the prospective or retrospective nature of the data collection, as well as the study sites (country of origin).

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 given 510(k) summary. For device performance validation, ground truth for glucose and cholesterol measurements would typically come from a central, certified clinical laboratory using a reference method (e.g., hexokinase method for glucose, enzymatic methods for cholesterol on a clinical chemistry analyzer). The "experts" would be the certified lab technicians and clinical pathologists overseeing these reference measurements, not typically individual radiologists.

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

This information is not applicable in the context of a blood glucose/cholesterol meter's performance study. Adjudication methods like 2+1 are relevant for subjective interpretations (e.g., imaging studies) where multiple readers assess and resolve discrepancies. For quantitative measurements, the ground truth is established by a reference laboratory method, not by expert consensus on the device's readings.

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 is not applicable to the Q.STEPS™ Biometer G/C Dual Monitoring System. This device is a standalone measurement system, not an AI-assisted diagnostic tool that aids human readers in interpreting images or complex data. Therefore, an MRMC study comparing human performance with and without AI assistance would not be relevant.

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

Yes, the performance evaluation for this device is inherently a standalone performance study. The device measures glucose and cholesterol levels; its accuracy and precision are assessed independently against reference laboratory methods. There is no "human-in-the-loop" in the sense of the algorithm providing an interpretation that a human then uses. The human simply uses the device and reads the displayed result.

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

The ground truth for this device's performance study would be based on reference laboratory methods for glucose and cholesterol. Specifically:

• Glucose: Likely measured using a reference method such as the hexokinase method on a certified clinical chemistry analyzer.
• Cholesterol: Likely measured using an enzymatic reference method on a certified clinical chemistry analyzer.

This is the standard for evaluating the accuracy of point-of-care or home-use blood chemistry devices.

8. The sample size for the training set

This information is not provided in the given 510(k) summary. For electrochemical biosensors, concept of a "training set" in the context of machine learning isn't directly applicable in the same way as for an AI algorithm. However, significant calibration and validation data would be collected during the device's development and manufacturing. If the manufacturer used advanced algorithms for signal processing that could be "trained" or optimized, data for such a process would exist but is not detailed here.

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

As described above, for a device like this, the "ground truth" for any calibration or validation would be established by comparison to reference laboratory methods (e.g., hexokinase for glucose, enzymatic methods for cholesterol). This ensures that the device's measurements correlate accurately with established clinical standards.

In summary, the provided 510(k) summary extract is a high-level overview and does not contain the detailed performance study information requested. Such information would typically be found in a separate, more comprehensive performance study report that accompanies the 510(k) submission but is not part of this "summary" document.

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