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The LIAISON® EBV IgM Serum Control Set (negative and positive) is intended for use as assayed quality control samples to monitor the performance of the LIAISON® EBV IgM assay on the LIAISON® Analyzer family.

The LIAISON® EBV IgM Serum Control Set (negative and positive) consists of liquid ready-to-use controls in human serum. The negative control is intended to provide an assay response characteristic of negative patient specimens and the positive control is intended to provide an assay response characteristic of positive patient specimens.

The controls are designed for use with DiaSorin LIAISON® EBV IgM assay on the LIAISON® analyzer family.

The provided text describes a 510(k) premarket notification for a medical device: the LIAISON® EBV IgM Serum Control Set. This document is a regulatory filing, not a research paper detailing a study of an AI/ML powered device. As such, many of the requested details regarding acceptance criteria, study design for AI models, human expert involvement, and specific performance metrics for an AI system are not present or applicable.

The document discusses analytical validation of a quality control material for an immunoassay, not an AI/ML algorithm. The "performance data" sections refer to studies demonstrating the utility and stability of this control material itself, rather than testing the performance of an AI system against clinical ground truth.

Therefore, for aspects related to AI/ML device performance validation, the document does not contain the necessary information. I will, however, extract the information that is present and relevant to the closest interpretation of the prompt for a non-AI device.

Here's what can be extracted and what information is missing:

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

The document mentions "predetermined acceptance criteria" and that the modified device "meets" them, but does not provide a specific table of these criteria or the numerical performance results against them. It lists the types of studies conducted:

2. Sample size used for the test set and the data provenance:

• Sample size for test set: Not explicitly stated. The studies mentioned (Commutability, Precision, Control Value Assignment, Control Range Definition, Stability) would involve a certain number of runs/measurements, but the specific number of "samples" (clinical specimens or control lots) used in these analytical studies is not provided.
• Data Provenance (e.g., country of origin, retrospective/prospective): Not specified. These are analytical studies of a quality control material which typically use manufactured lots of the product and potentially stored clinical samples for commutability, so the concept of "country of origin of data" is less relevant than for patient-derived datasets. Whether the studies were retrospective or prospective is not stated.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable: For a quality control material, the "ground truth" is typically established by the assigned values and ranges based on the manufacturing process and extensive analytical characterization, not by human experts adjudicating clinical cases.

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

• Not applicable: There is no mention of adjudication, as this is pertinent to human review of clinical data, which is not the focus of this device's validation.

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 a quality control material for an immunoassay, not an AI-powered diagnostic tool. Therefore, MRMC studies or human reader improvement with AI assistance 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 AI algorithm.

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

• For the performance of the control material itself: The "ground truth" is based on analytical characterization of the control material (e.g., assigned values, measurement of precision, stability over time) using the LIAISON® EBV IgM assay and Analyzer family, consistent with established laboratory quality control practices. For commutability, it would likely involve testing of diverse clinical samples along with the controls to ensure they behave similarly.

8. The sample size for the training set:

• Not applicable: As this is not an AI/ML device, there is no concept of a "training set" for an algorithm.

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

• Not applicable: See above.

In summary, the provided document details the regulatory approval (510(k)) of a quality control material, not an AI/ML device. Therefore, the questions designed to probe the validation of an AI/ML system are largely not applicable to this document. The "acceptance criteria" and "performance data" mentioned refer to the analytical performance of the control material itself (e.g., its precision, stability, and commutability with patient samples) rather than the clinical performance of a diagnostic or AI system.

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