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The Preciset® Serum Proteins calibrator is intended to be used in the calibration of immunoturbidimetric assays on the BM/Hitachi systems.

The Preciset® Serum Proteins consists of multi-level calibrators based on human serum. The concentrations of the components have been adjusted to ensure optimal calibration of the following immunoturbidimetric assays: Tina-quant® IgA, Tina-quant® IgG, Tina-quant® IgM, Tina-quant® C3, and Tina-quant® C4, Transferrin, and C-Reactive Protein (CRP).

The provided text is a 510(k) summary for the Preciset® Serum Proteins calibrator. It describes the device, its intended use, and its substantial equivalence to a predicate device. However, this document does not contain information about acceptance criteria, device performance studies to meet such criteria, sample sizes, expert involvement, or ground truth establishment.

The document focuses on the regulatory submission process for a calibrator, which is a device used to establish calibration curves for quantitative determinations. The "performance" in this context refers to the stated values of constituent analytes in the calibrator, not a diagnostic accuracy performance as might be seen for devices like imaging AI. Therefore, most of the requested information is not applicable or not present in this type of submission.

Here's a breakdown of what can and cannot be answered based on the provided text:

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

• Acceptance Criteria: Not explicitly stated in terms of performance metrics like sensitivity, specificity, or clinical accuracy. For calibrators, acceptance criteria would typically revolve around the accuracy and stability of the assigned analyte values, and their ability to produce valid calibration curves on specified instruments. This document does not include these specific criteria or the studies that demonstrate compliance.
• Reported Device Performance: The "performance" for a calibrator is presented as the representative constituent analyte levels used in the calibrator, which are listed in the table below. This is not performance as in diagnostic accuracy, but rather the intrinsic property of the calibrator.

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 / Not Provided: This document does not describe performance studies involving "test sets" in the context of diagnostic accuracy. The data provenance is not mentioned.

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 / Not Provided: This information is not relevant for a calibrator's regulatory submission, as there's no "ground truth" to establish through expert consensus as there would be for a diagnostic tool. The values for the calibrator analytes are determined through established analytical methods.

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

• Not Applicable / Not Provided: This is not relevant for a calibrator.

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 calibrator, not an AI-assisted diagnostic device.

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

• Not Applicable: This is a calibrator, not an algorithm.

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

• Not Applicable / Not Provided: For a calibrator, the "ground truth" for the analyte concentrations is established through rigorous analytical measurement processes and metrological traceability, not through clinical expert consensus, pathology, or outcomes data. The specific methods used to assign values are not detailed in this summary.

8. The sample size for the training set

• Not Applicable: This device is a calibrator, not an algorithm that requires a training set.

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

• Not Applicable: This device is a calibrator, not an algorithm that requires a training set.

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The Preciset® Serum Proteins calibrator is intended to be used in the calibration of immunoturbidimetric assays on the BM/Hitachi systems.

The Preciset® Serum Proteins consists of multi-level calibrators based on human serum. The concentrations of the components have been adjusted to ensure optimal calibration of the following immunoturbidimetric assays: Tina-quant® IgA, Tina-quant® IgG, Tina-quant® IgM, Tina-quant ® C3, and Tina-quant ® C4.

The provided 510(k) summary for the "Preciset® Serum Proteins" calibrator focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study of its performance against specific acceptance criteria. This type of submission, common for calibrators and certain other IVDs, often relies on comparing the new device's formulation and intended use to an already cleared predicate.

Therefore, many of the requested details about acceptance criteria, clinical studies, and ground truth establishment are not applicable to this 510(k) submission.

Here's a breakdown of the available information in relation to your request:

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

• Acceptance Criteria: Not explicitly stated as formal performance acceptance criteria (e.g., accuracy, precision targets). The core "acceptance criteria" for a calibrator in this context is its ability to accurately calibrate the specified immunoturbidimetric assays. This is implied by its composition and comparison to the predicate.
• Reported Device Performance: Instead of performance metrics, the document provides the representative constituent levels for each analyte (IgG, IgA, IgM, C3, C4) at different "levels" (1 through 5, or 4 for C3 and C4). These levels are what the calibrator is designed to provide for calibration, not a measure of its performance against a gold standard. The fact that the device was cleared implies that these levels were deemed appropriate for its intended use.

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

• N/A. This document does not describe a clinical performance study using a test set of patient samples. The device is a calibrator, and its evaluation typically involves analytical studies (e.g., accuracy of manufactured concentrations, stability) rather than clinical studies on patient samples.

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

• N/A. No test set requiring expert ground truth is described. The "ground truth" for the calibrator's constituent levels would be established through accredited reference materials and methods, as indicated by the "Reference Material: RPPHS" in the table.

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

• N/A. No test set or adjudication method is described.

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

• N/A. This device is a chemical calibrator, not an AI-assisted diagnostic tool.

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

• N/A. This is not an algorithm-based device.

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

• The "ground truth" for the calibrator's constituent levels is based on Reference Material (RPPHS). This indicates that the concentrations of the analytes (IgG, IgA, IgM, C3, C4) in the calibrator were determined and traced to a recognized reference standard (Reference Preparation for Human Serum Proteins). This is a common and appropriate method for establishing the "truth" for calibrator values.

8. The sample size for the training set

• N/A. This device does not use a training set in the context of machine learning or AI.

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

• N/A. As above, no training set in the context of machine learning. The "ground truth" for the calibrator's composition is linked to Reference Material (RPPHS).

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