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The CEDIA Cardiac TDM Multi-Cals are used to calibrate the CEDIA assays for digoxin, N-acetylprocainamide, and procainamide, in human serum and plasma.

The CEDIA Cardiac TDM Calibrators are manufactured using bovine serum albumin, digoxin, N-acetylprocainamide, procainamide, stabilizers, and preservatives. The drugs are appropriately spiked into the calibrator matrix to the correct calibrator concentration levels. The calibrators are in process checked and quality controlled against in-house reference calibrators (prepared using a similar procedure) which have been value assigned by comparison to the predicate device to ensure correct assay calibration.

The provided text is a 510(k) Summary for the CEDIA Cardiac TDM Multi-Cals, which are calibrators for therapeutic drug monitoring assays. This document focuses on demonstrating substantial equivalence to predicate devices, not on the performance of a diagnostic device that detects or measures a condition. Therefore, many of the requested elements for describing acceptance criteria and a study proving device performance (like sensitivity, specificity, or human reader improvement with AI) are not applicable to this type of submission.

However, I can extract the relevant information regarding the "performance characteristics" and the comparison to predicate devices, which serve as the "acceptance criteria" for a calibrator.

Here's a breakdown of the available information based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

For calibrators, performance is primarily assessed by their ability to provide equivalent calibration to existing, approved calibrators. The key acceptance criterion is "equivalent correlation" between the new calibrators and the predicate device's calibrators.

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

The document states, "Specific data on the performance of the test have been incorporated into the draft labeling in attachment 5." However, the actual sample size, details on the test set, and data provenance (e.g., country of origin, retrospective/prospective) are not provided in the excerpt.

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

Not applicable/Not provided. For calibrators, "ground truth" is typically defined by reference methods or established values, not expert consensus in the same way it would be for a diagnostic image or clinical finding. The calibrators are "value assigned by comparison to the predicate device."

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

Not applicable/Not provided. This concept is generally used in studies involving human interpretation or subjective assessments, which is not the primary focus of calibrator validation.

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

No. This is not an AI device, nor is it a diagnostic device that humans interpret. Therefore, an MRMC study and AI assistance effect size are not applicable.

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

Yes, in essence. The testing of the calibrators themselves against predicate calibrators constitutes a "standalone" evaluation of their performance in achieving equivalent calibration. There is no "human-in-the-loop" component in the calibrator's function itself. The calibrator's performance is objective against an established reference.

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

The "ground truth" for the CEDIA Cardiac TDM Multi-Cals is established by comparison to the predicate device's kit calibrators. The predicate calibrators themselves would have had their values established through validated analytical methods and reference standards. The document states the new calibrators "have been value assigned by comparison to the predicate device to ensure correct assay calibration."

8. The sample size for the training set

Not applicable/Not provided. This is not a machine learning or AI device that requires a training set. The calibrators are manufactured reagents.

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

Not applicable/Not provided. As stated above, there is no training set for this type of device.

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