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The Elecsys® C-Peptide CalCheck is intended for use in the verification of the calibration established by the Elecsys® C-Peptide reagent on the Elecsys® immunoassay systems.

The Elecsys® C-Peptide CalCheck is a lyophilized product consisting of synthetic human C-Peptide in a buffered equine serum matrix. During manufacture, the analytes are spiked into the matrix at the desired concentration levels.

The provided 510(k) summary for the Elecsys® C-Peptide CalCheck describes a quality control material intended for use in verifying the calibration of the Elecsys® C-Peptide reagent on Elecsys® immunoassay systems.

It is important to note that this submission is for a Class I medical device (quality control material), which typically has different performance evaluation requirements compared to diagnostic algorithms using AI. The "acceptance criteria" and "study that proves the device meets the acceptance criteria" in this context refer to the analytical performance characteristics of the control material (value assignment and stability) rather than the clinical performance of a diagnostic algorithm.

Based on the provided documents, here's the information requested:

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

The document mentions "Performance Characteristics: The Elecsys® C-Peptide CalCheck was evaluated for value assignment and stability." However, specific quantitative acceptance criteria (e.g., % recovery, stability limits) and the detailed reported performance data are not explicitly provided in the excerpt. The submission focuses on establishing substantial equivalence to a predicate device (Elecsys® SHBG CalCheck) based on characteristics like intended use, levels, format, handling, and stability, rather than detailing the analytical validation results of C-Peptide CalCheck itself.

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

This information is not provided in the excerpt. For a quality control material, a "test set" would typically refer to the number of lots or units tested for value assignment and stability studies.

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 is not applicable/provided for this type of device. The "ground truth" for a quality control material is its assigned value, which is determined through an analytical process by the manufacturer, not by expert consensus in the typical sense of diagnostic image interpretation.

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

This is not applicable/provided for this type of device. Adjudication methods are typically used in clinical studies involving interpretation by multiple readers/experts.

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

An MRMC comparative effectiveness study is not applicable for this device. This type of study is relevant for diagnostic devices (especially those involving AI or human interpretation) to assess reader performance.

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

This is not applicable. The Elecsys® C-Peptide CalCheck is a physical quality control material, not a diagnostic algorithm.

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

For a quality control material, the "ground truth" for its assigned value is established through rigorous analytical methods and reference measurements by the manufacturer leading to a "value assignment." This would involve multiple measurements on characterized reference instruments/methods, likely performed by trained laboratory personnel, rather than expert clinical consensus or pathology.

8. The sample size for the training set

This is not applicable. As a quality control material, there is no "training set" in the context of machine learning or AI models.

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

This is not applicable.

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