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In-vitro diagnostic reagents for the quantitative determination of immunoglobulins (IgG. IgA and IgM) in human serum, heparinized and EDTA plasma, and IgG in human urine and cerebrospinal fluid (CSF) by means of immunonephelometry on the BN II and BN ProSpec® System. Measurements of IgG aid in the diagnosis of abnormal protein metabolism and the body's lack of ability to resist infectious agents.

The N Antiserum to Human IgG reagent containing animal serum, produced by immunization of rabbits with highly purified human immunoglobulin (

The provided text describes a special 510(k) premarket notification for a modified device, "N Antisera to Human Immunoglobulins (IgG, IgA, and IgM)". The sole modification is the addition of a High Dose Hook (HDH) effect claim for IgG in cerebrospinal fluid (CSF) samples.

Here's a breakdown of the requested information based on the provided document:

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

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

• Sample Size: The study used a "CSF high sample pool." The exact number of individual patient samples contributing to this pool (if multiple were pooled) is not specified. However, the study involved a dilution scheme with twelve (12) individual dilution levels, including the neat sample.
• Data Provenance: Not explicitly stated. The manufacturer is Siemens Healthcare Diagnostics Products GmbH, located in Marburg, Germany, which suggests the study was likely conducted in Germany or a similar geographic region. It is implicitly a prospective study designed to evaluate the HDH effect for the modified 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)

This section is not applicable as the device is an in-vitro diagnostic reagent for quantitative determination, not an imaging or interpretive device that would typically require expert ground truth establishment in the described manner. The "ground truth" for this type of test is the quantitatively measured concentration of IgG in a sample, established through laboratory methods and comparison to known standards.

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

This section is not applicable. Adjudication methods like 2+1 or 3+1 are typically used for establishing ground truth in interpretive studies (e.g., radiologists reviewing images). For a quantitative in-vitro diagnostic test, the "ground truth" is determined by the analytical method itself against known standards.

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. The device is an in-vitro diagnostic reagent, not an AI-assisted diagnostic tool or an imaging device requiring human reader interpretation. No MRMC study was performed.

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

This refers to the performance of the analytical system (N Antisera reagent on BN II System) without human intervention in the measurement process. The HDH study performed is a standalone performance evaluation of the reagent/instrument system. The acceptance criteria and performance data in the table above demonstrate this standalone performance.

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

For this in-vitro diagnostic device, the "ground truth" for the High Dose Hook study was established by creating known dilutions of a high-concentration CSF sample, which were then measured by the device. The reported concentrations for these dilutions serve as the reference. The ultimate analytical ground truth for the quantitative measurement itself is tied to international standards like ERM-DA470k/IFCC (as stated in the "Traceability/Standardization" section).

8. The sample size for the training set

This document does not describe the development or training of a machine learning model, so there is no training set in the typical sense. The "training" for such a diagnostic test involves method development, optimization, and validation using various samples and controls, but these are not referred to as a "training set" for an algorithm.

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

As there is no training set for an algorithm, this question is not applicable.

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