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CR-S reagent, when used in conjunction with UniCel® DxC 600/800 System(s) and SYNCHRON® Systems AQUA CAL 1 and 2, is intended for the quantitative determination of creatinine concentration in human serum, plasma or urine.

The SYNCHRON Systems CR-S Reagent is designed for optimal performance on the SYNCHRON UniCel DxC (UniCel DxC 600, DxC 800, UniCel DxC 600i) Systems. The reagent kit contains two 300-test cartridges, and is packaged separately from the associated calibrators.

The provided 510(k) summary for the SYNCHRON® Systems Creatinine Reagent (K071283) does not contain detailed information regarding acceptance criteria, specific performance metrics, or a comprehensive study report with sample sizes, ground truth establishment, or expert involvement as typically found in studies for AI-powered diagnostic devices.

This document describes a modification to an existing creatinine reagent and focuses on demonstrating substantial equivalence to the predicate device, rather than providing a detailed clinical study for a novel device. The modification primarily involves changes in system parameters and calibration methodology.

Therefore, many of the requested categories cannot be fully addressed from the provided text.

Here's the information that can be extracted or reasonably inferred from the document, along with an explanation of why other requested information is not available:

1. Table of Acceptance Criteria and Reported Device Performance

The document states: "Performance data from validation testing supports equivalency." However, it does not provide a table of specific acceptance criteria or quantitative reported device performance metrics (e.g., sensitivity, specificity, accuracy, precision data, limits of detection, linearity ranges that would typically be detailed in a robust study report). It only broadly claims that the data "supports equivalency."

2. Sample Size Used for the Test Set and Data Provenance

This information is not provided in the document. The filing focuses on the modification of a reagent, not a clinical study involving a "test set" in the context of AI device evaluation.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided and is not applicable in the context of a chemical reagent modification. Ground truth, in the AI diagnostic sense, would refer to definitive diagnoses or measurements established by human experts or gold standard methods for comparison with AI output. For a creatinine reagent, performance is typically assessed against established analytical methods traceable to international standards (like IDMS mentioned for calibrator material).

4. Adjudication Method for the Test Set

This information is not provided and is not applicable for a chemical reagent modification. Adjudication methods (e.g., 2+1, 3+1) are common in AI diagnostic image reading studies where human readers resolve discrepancies.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

An MRMC study was not performed. This type of study is relevant for evaluating the impact of AI assistance on human reader performance, which is not applicable to a chemical reagent.

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

This concept is not applicable to a chemical reagent test. "Standalone performance" refers to the algorithm's performance without human interaction, a concept for AI diagnostics, not for a laboratory reagent. The device itself (the reagent in combination with the analyzer) is designed to perform the measurement quantitatively.

7. The Type of Ground Truth Used

The document alludes to the traceability of calibrator values to "Isotope Dilution Mass Spectrometry (IDMS) for creatinine recovery." IDMS is a highly accurate and precise analytical method often considered a "gold standard" for creatinine measurement. While this isn't "ground truth" in the AI diagnostic sense, it suggests that the calibration and potentially the accuracy validation of the reagent would be referenced against this highly accurate analytical method.

8. The Sample Size for the Training Set

This information is not provided and is not applicable. Chemical reagents are not "trained" in the machine learning sense. Their performance is inherent in their chemical composition and reaction kinetics when used with the specified analytical system.

9. How the Ground Truth for the Training Set Was Established

This information is not provided and is not applicable. As stated above, chemical reagents are not "trained" using a "training set" with established ground truth in the way an AI algorithm is. Their accuracy and performance are established through rigorous analytical validation studies.

In summary:

The provided document, a 510(k) summary for a reagent modification, focuses on demonstrating substantial equivalence through analytical performance data, not a clinical study involving experts, test sets for AI evaluation, or ground truth establishment in the context of diagnostic imaging. Therefore, most of the requested information pertinent to AI device evaluation is not present. The key takeaway is that the device's performance data "supports equivalency" to a predicate device, and its calibrator values are traceable to IDMS, indicating a high level of analytical accuracy reference.

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