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The Dimension® AHDL Calibrator is an in vitro diagnostic product intended to be used to calibrate the Automated High Density Lipoprotein Cholesterol (AHDL) method for the Dimension® clinical chemistry system

The Dimension® AHDL Calibrator is a liquid bovine serum albumin based product. The Level 1 calibrator contains no detectable HDL Cholesterol. Levels 2 and 3 contain human HDL Cholesterol. The kit consists of six vials; two at each of three levels, containing 1 mL each.

The provided text is for a medical device called "Dimension® AHDL Calibrator," which is a calibrator for a clinical chemistry system. This type of device is used to ensure the accuracy of other diagnostic tests and is not a diagnostic device itself that makes clinical findings. As such, the typical "acceptance criteria" and "study" questions you've posed (like performance metrics, ground truth, expert adjudication, MRMC studies, standalone performance, etc.) are not applicable in the way they would be for a diagnostic algorithm that interprets medical images or other patient data.

Instead, the submission focuses on substantial equivalence to a predicate device. This means the primary "acceptance criterion" is that the new device performs sufficiently similarly to a legally marketed device already on the market, such that it raises no new questions of safety or effectiveness.

Here's how the information provided relates to your questions, adapted for a calibrator device:

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

For a calibrator, "performance" is assessed primarily by comparing its characteristics and intended use to a predicate device. The fundamental acceptance criterion for this 510(k) submission is that the Dimension® AHDL Calibrator is substantially equivalent to the Dimension® HDL 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 in the traditional sense. This is a calibrator, not a diagnostic algorithm that processes patient data. The "test set" for a calibrator would typically involve analytical performance studies internally (e.g., stability, homogeneity, value assignment), rather than a patient-data-driven test set for diagnostic accuracy. The provided document does not detail specific analytical performance studies, but rather focuses on comparing the device's characteristics to a predicate.

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 in the context of diagnostic algorithms (e.g., disease presence/absence) is not relevant for a calibrator. The "truth" for a calibrator is its assigned value, which is determined through metrologically sound methods, often traceable to reference materials, rather than expert consensus on patient data.

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

• Not applicable. Adjudication methods are used to resolve discrepancies in diagnoses or interpretations of patient data, which is not the function of 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. An MRMC study is designed for diagnostic tools that involve human interpretation of medical cases, often with the assistance of AI. A calibrator does not involve human "readers" or "cases" in this context.

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

• Not applicable. This device itself is a calibrator, not a diagnostic algorithm. Its function is to allow a diagnostic instrument (the Dimension® clinical chemistry system) to perform accurately when testing patient samples.

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

• Not applicable in the provided document. If specific analytical performance studies were detailed (which they are not in this summary), the "ground truth" for a calibrator would typically involve its assigned values being traceable to higher-order reference materials, reference methods, or certified reference laboratories, thus ensuring accuracy and metrological traceability.

8. The sample size for the training set:

• Not applicable. Calibrators are developed and manufactured according to quality systems and analytical methods; they are not "trained" in the way an AI algorithm learns from labeled data.

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

• Not applicable. As calibrators are not "trained" with a training set of data like an AI model, the concept of establishing ground truth for a training set does not apply here.

In summary: The provided document is a 510(k) summary for a calibrator device. For such devices, the primary demonstration of safety and effectiveness lies in showing substantial equivalence to an already legally marketed predicate device, rather than through complex clinical performance studies with patient data and expert interpretations that would be typical for diagnostic algorithms. The information presented specifically supports the claim of substantial equivalence based on a comparison of device characteristics and intended use.

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