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The STERRAD® 100NX® DUO Cycle Test Pack is used for routine monitoring of the STERRAD® 100NX® DUO Sterilization Cycle and is also used for the periodic testing of a STERRAD® 100NX® System DUO Cycle, using hospital-defined loads containing devices that do not exceed claims of the cycle. The STERRAD® 100NX® DUO Cycle Test Pack consists of a STERRAD® CYCLESURE® 24 Biological Indicator, vial and cap to hold the BI.

The STERRAD® 100NX® DUO Test Pack consists of a CYCLESURE® 24 Biological Indicator (BI) and a STERRAD® NX® Test Vial with cap for holding the BI during the sterilization cycle.

Here's an analysis of the provided text regarding the acceptance criteria and the study that proves the device meets those criteria:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: "DUO Test Packs assembled from three lots of CYCLESURE® 24 Biological Indicator (BI)" were used for the resistance evaluation.
• Data Provenance: Not explicitly stated, but given that the applicant is Advanced Sterilization Products, a division of Ethicon, Inc. located in Irvine, CA, it is highly likely the testing was conducted prospectively and in the United States. The study description implies a laboratory or controlled setting.

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

There is no mention of human experts being used to establish the "ground truth" for this device. The ground truth is intrinsically defined by the biological model and the chemical indicator's expected behavior within the sterilization process. The "ground truth" here is the scientific measurement of biological indicator survival and chemical indicator color change.

4. Adjudication Method for the Test Set

Not applicable. There was no human adjudication as the assessment was based on objective biological and chemical responses.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is typically used for diagnostic or imaging devices where human interpretation is a key component, and the AI's impact on human performance is being measured. This device is a biological indicator for sterilization monitoring, not a diagnostic tool requiring human interpretation for its primary function.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, in essence. The entire evaluation focuses on the performance of the STERRAD® 100NX® DUO Cycle Test Pack product itself, which functions as a standalone indicator of sterilization effectiveness. Its performance is measured directly against established biological models and chemical indicator specifications, without human interaction influencing the core function it is designed to evaluate (i.e., whether sterilization occurred). The "algorithm" here isn't a complex AI, but rather the intrinsic biological and chemical response mechanisms of the test pack.

7. The Type of Ground Truth Used

The ground truth used is based on:

• Biological Model: The established and validated "biological model developed for the DUO Cycle" represents the benchmark for resistance to the sterilization process. This model likely involves characterized microorganisms and their known inactivation kinetics.
• Expected Chemical Indicator Response: The predetermined, appropriate response of the chemical indicator to the sterilization conditions (specifically half-cycle parameters).

8. The Sample Size for the Training Set

There is no explicit mention of a "training set" in the context of an AI/ML algorithm. This device is not an AI/ML product. The testing involves physical devices (biological indicators) rather than training data for an algorithm. The "training" in this context would be the historical data and scientific principles used to develop the biological model itself, but that's not described here.

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

Not applicable, as there is no explicitly defined "training set" for an AI/ML algorithm. The "ground truth" for the device's functional evaluation (as described in point 7) is established through scientific validation of the biological model and the chemical indicator's properties, likely using established standards and lab procedures.

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