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Eon Cholesterol Reagent is an in vitro diagnostic device intended for the quantitative determination of total cholesterol in human plasma and serum using the Eon 100 Clinical Chemistry Analyser. Cholesterol measurements are used in the diagnosis and treatment of disorders involving excess cholesterol in the blood and lipid and lipoprotein metabolism disorders.

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This is a 510(k) clearance letter for the Eon Cholesterol Reagent, an in vitro diagnostic device. This type of document typically provides regulatory approval based on demonstrating substantial equivalence to a predicate device, rather than detailed performance studies and acceptance criteria as might be seen for novel or higher-risk devices, especially those involving AI/ML.

Therefore, the specific information requested in the prompt, especially regarding AI/ML aspects like training sets, expert adjudication, MRMC studies, and standalone performance, is not present in the provided document because it describes a chemical reagent, not an AI/ML device.

However, I can extract what is available about performance and acceptance criteria for this specific type of device.

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

For in vitro diagnostic reagents like the Eon Cholesterol Reagent, acceptance criteria typically involve demonstrating analytical performance characteristics (e.g., accuracy, precision, linearity) that are comparable to a legally marketed predicate device. The document states that the device is "substantially equivalent" to predicate devices, implying that its performance meets the regulatory expectations for such a comparison. However, the specific numerical acceptance criteria (e.g., ±X% accuracy, CV < Y%) and the detailed reported performance data are not provided in this regulatory letter. These details would typically be found in the 510(k) submission document itself, which is not publicly available here.

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

The document does not specify the sample size used for any test set or the data provenance. For an in vitro diagnostic device, test sets would typically involve patient samples (serum/plasma, as indicated for this device) from various demographic groups, often across different clinical settings. Whether the data was retrospective or prospective is also not mentioned.

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

This document describes a chemical reagent for quantitative determination of cholesterol, not an AI/ML algorithm requiring expert interpretation of images or signals to establish ground truth. The "ground truth" for a cholesterol measurement device would be established by reference methods or highly accurate laboratory instruments, not by human experts adjudicating results in the way machine learning models are evaluated. No information on experts or their qualifications is provided or relevant in this context.

4. Adjudication method for the test set

Not applicable for this type of device (chemical reagent). Ground truth for cholesterol measurement is typically established by established laboratory methods, not by human adjudication of interpretations.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done

No, an MRMC study is not relevant for this type of device. MRMC studies are used to evaluate diagnostic imaging systems or AI tools that assist human readers in interpreting complex visual information. The Eon Cholesterol Reagent is an automated in vitro diagnostic test.

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

This question is framed for AI/ML algorithms. For a chemical reagent, the "standalone performance" refers to the analytical performance of the assay itself on an automated analyzer. The device described, the "Eon Cholesterol Reagent," is intended for use with the "Eon 100 Clinical Chemistry Analyser," indicating it's an automated test. The performance would be assessed purely through its analytical capabilities, not as an "algorithm only" in the AI sense. The document does not provide the details of this standalone analytical performance, only the FDA's determination of substantial equivalence.

7. The type of ground truth used

For a cholesterol reagent, the ground truth would typically be established by:

• Reference methods: Highly accurate and precise laboratory methods (e.g., isotope dilution mass spectrometry, IDMS) that serve as a gold standard.
• Certified reference materials: Materials with an accurately known concentration of cholesterol.
• Comparison to a legally marketed predicate device: As indicated by the "substantial equivalence" determination, the performance of the Eon Cholesterol Reagent would have been compared to an existing, cleared device, implying that the predicate's results served as a comparison point (though not strictly "ground truth" in the primary sense).

The document itself does not specify which type of ground truth was used for testing.

8. The sample size for the training set

Not applicable. This device is a chemical reagent, not an AI/ML algorithm. There is no "training set" in the machine learning sense.

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

Not applicable, as there is no training set for this type of device.

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