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The Nova StatStrip Glucose Hospital Meter is intended for in vitro diagnostic use by health care professionals and for Point-Of-Care usage for the quantitative measurement of glucose in capillary, venous and arterial whole blood. It is indicated for use in a clinical setting by healthcare professionals as an aid to monitor the effectiveness of diabetes control.

Nova StatStrip Glucose Test Strips are intended for use only with the StatStrip Glucose Hospital Meter for quantitative tests. The glucose meter is intended to quantitatively measure glucose (sugar) in whole blood. The Glucose Meter is calibrated to provide plasma equivalent results to laboratory methods. Nova StatStrip Glucose Test Strips are for testing outside the body (in vitro diagnostic use only).

Nova StatStrip Control Solutions is intended for use with the Nova StatStrip Glucose Hospital Meter and Nova StatStrip Glucose Test Strips as a quality control check to verify the accuracy of blood glucose test results. There are three levels of controls, (Level 1, Level 2, and Level 3). These solutions will be offered for sale separately from the meter.

Nova StatStrip Glucose Linearity Kit solutions are used to check the linearity of the Nova StatStrip Glucose Hospital Meter System.

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This is a 510(k) premarket notification for a glucose meter and does not contain the detailed study information typically found in a clinical trial report or a scientific publication describing device performance and acceptance criteria. The provided document is an FDA clearance letter and an Indications for Use statement.

Therefore, I cannot provide a table of acceptance criteria and reported device performance or details about a study that proves the device meets specific criteria based solely on the provided text. The document states that the FDA reviewed the premarket notification and determined the device is "substantially equivalent" to legally marketed predicate devices, which is the basis for its clearance, rather than proof of meeting specific, pre-defined acceptance criteria in a detailed study report within this document.

For a glucose meter, acceptance criteria typically relate to accuracy (e.g., bias, precision, and agreement with a reference method as per ISO 15197 for point-of-care blood glucose monitoring systems) and other performance characteristics, but these specific criteria and the data to prove them are not in this clearance letter.

To answer your specific questions in the format requested, based only on the provided text, I can state:

1. Table of acceptance criteria and the reported device performance:
   Not provided in this document. The document primarily concerns the FDA's substantial equivalence determination, not a detailed presentation of performance data against specific acceptance criteria.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):
   Not provided in this document.

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):
   Not applicable or not provided. For a glucose meter, ground truth is typically established by laboratory reference methods (e.g., YSI 2300 STAT Plus Glucose & Lactate Analyzer), not expert readers.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
   Not applicable or not provided.

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. This is a glucose meter, not an AI-assisted diagnostic imaging device.

6. If a standalone (i.e. algorithm only, without human-in-the-loop performance) was done:
   A glucose meter inherently operates "standalone" in generating a quantitative reading. However, the details of its standalone performance testing (e.g., accuracy against a reference method) are not included in this document.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
   While not explicitly stated in this document, for glucose meters, the ground truth is typically established using a high-precision laboratory reference method (e.g., hexokinase method on a central laboratory analyzer).

8. The sample size for the training set:
   Not applicable or not provided. This device is likely using established electrochemical principles; modern machine learning "training sets" are not typically used for this type of device in the same way they would be for an AI algorithm.

9. How the ground truth for the training set was established:
   Not applicable or not provided.

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