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Precinorm ® PUC (Proteins in Urine/CSF) is for use in quality control by monitoring accuracy and precision for the quantitative methods as specified in the enclosed value sheet. Precipath ® and proteins in Urine/CSF) is for use in quality control by monitoring accuracy and precision for the quantitative methods as specified in the enclosed value sheet.

Precinorm ® PUC/ Precipath ® PUC is a liquid ready-for-use control based on a buffered aqueous solution. Concentrations of control components have been adjusted to represent normal and pathological ranges.

The provided 510(k) summary (K041812) describes a quality control material (Precinorm® Proteins in Urine/CSF (PUC) and Precipath® Proteins in Urine/CSF (PUC)), not an AI/ML powered device. As such, the concept of "acceptance criteria," "study that proves the device meets acceptance criteria," "sample size for test set," "data provenance," "number of experts for ground truth," "adjudication method," "MRMC comparative effectiveness study," "standalone performance," "type of ground truth," "training set size," and "ground truth for training set" in the context of AI/ML performance metrics are not applicable to this submission.

This submission is for a quality control material, which is a diagnostic product used to monitor the performance of laboratory tests. The summary focuses on demonstrating "substantial equivalence" to a predicate device, as required for 510(k) submissions.

Here's the relevant information based on the provided document:

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

The concept of "acceptance criteria" for performance metrics like sensitivity, specificity, etc., and "reported device performance" are not applicable here. Instead, the "acceptance criteria" are implied by the demonstration of substantial equivalence to a predicate device in terms of intended use, format, stability, and constituent analytes.

The "reported device performance" is the claim that the modified device is "substantially equivalent" to the predicate device based on these characteristics. The key difference noted is the addition of Immunoglobulin A and Immunoglobulin M to Precipath®. The assumption is that the new formulation maintains similar performance characteristics for the common analytes and that the new analytes perform as expected for a quality control material.

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 is not a study assessing diagnostic accuracy with a test set of patient data, but rather a submission for a quality control material. No patient samples or clinical data are mentioned for establishing performance.

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. Ground truth as typically understood in AI/ML is not relevant for this type of device. The "ground truth" for a quality control material is its accurately assigned values for the analytes it controls, which would be established through robust analytical methods by the manufacturer.

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

Not applicable. No expert adjudication for a test set of patient data is mentioned or relevant.

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 not an AI-powered diagnostic device, and therefore, MRMC studies are not relevant.

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

Not applicable. This is not an algorithm, but a physical quality control material.

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

Not applicable. The ground truth for this device would be the accurately determined concentrations of the analytes within the control material, established through analytical laboratory methods, not clinical outcomes or expert consensus on patient data.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires a training set.

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

Not applicable. This is not an AI/ML device, and no training set is used.

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