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The VALIDATE Lipoprotein Calibration Verification Test Set is intended for in vitro diagnostic use in quantitatively verifying calibration, validating reportable ranges, and determining linearity on clinical chemistry for the following analytes: cholesterol, HDL-cholesterol, LDLcholesterol, Apolipoprotein A (Apo-A), and Apolipoprotein B (Apo-B).

The VALIDATE Lipoprotein Calibration Verification Test Set is used by trained laboratory professionals for the quantitative determination of linearity, calibration verification and verification of reportable range in manual, semi-automated and automated clinical chemistry systems for the following analytes: cholesterol, HDL cholesterol, LDL cholesterol, Apolipoprotein A, and Apolipoprotein B.

Quality control material (assayed and unassayed). VALIDATE Lipoprotein Calibration Test Set is a human protein serum based calibration verification test set containing multiple levels used to establish the relationship between theoretical operation and actual performance of the included analytes. Each set contains one bottle each of six (6) levels, including zero. Each bottle contains 5 milliliters. There exists a linear relationship among each set of solutions.

This document is a 510(k) summary for the VALIDATE Lipoprotein Calibration Verification Test Set. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study proving the device meets specific acceptance criteria for performance in a clinical setting.

Therefore, many of the requested details about acceptance criteria, study design, and performance metrics (like sample size for test/training sets, expert involvement, MRMC studies, or standalone performance) are not directly applicable or available in this type of submission. The device is a quality control material, not a diagnostic algorithm.

Here's a breakdown of the available information based on the prompt:

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

This document does not present explicit "acceptance criteria" in terms of performance thresholds (e.g., sensitivity, specificity, accuracy) that a diagnostic device would typically have, nor does it present specific performance data beyond stating substantial equivalence. The "performance" described is the intended function of the calibration verification test set, which is to verify calibration, validate reportable ranges, and determine linearity.

The comparison table provided in the submission (Page 1) focuses on characteristics of the device and its predicate:

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

Not applicable. This submission describes a quality control material, not a diagnostic device that processes patient data. Therefore, there is no "test set" of patient data in the typical sense for evaluating diagnostic performance. The submission relies on a comparison of device characteristics to a legally marketed predicate device.

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. As this is not a diagnostic device involving interpretation of patient data, there were no experts establishing ground truth for a test set.

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

Not applicable.

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 device is not an AI diagnostic tool and does not involve human readers in this context.

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

Not applicable. This device is a physical calibration verification test set, not a software algorithm.

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

Not applicable. The "ground truth" for a calibration verification material would be its precisely manufactured and assigned values for the analytes. The submission implies these values are established through the manufacturing process, but details on how these specific values are determined are not part of this 510(k) summary.

8. The sample size for the training set:

Not applicable. There is no "training set" as this is not an AI/machine learning device.

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

Not applicable.

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