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The modified Stöckert S5 System is intended to be used during cardiopulmonary bypass for procedures lasting six (6) hours or less.

The Stöckert Gas Blender is an optional accessory to and designed to be operated with the Stöckert S5 System. It cannot be operated independently from the heart lung machine console.

The provided text describes a 510(k) summary for a medical device (Stöckert S5 System Gas Blender) and a letter from the FDA regarding its substantial equivalence. This document focuses on regulatory approval based on demonstrating equivalence to an existing device, rather than presenting a performance study with detailed acceptance criteria and results as typically seen for novel AI/ML medical devices.

Therefore, many of the requested sections (e.g., sample size for test set, number of experts for ground truth, MRMC study, training set details) are not applicable or not available in this type of regulatory submission. The information provided is primarily related to design verification and validation, ensuring the device meets its own specifications and is safe and effective when compared to a predicate device.

Here's an attempt to answer the questions based only on the provided text, acknowledging the limitations:

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

The document states that "Design verification and validation information provided in this Special 510(k): Device Modification demonstrates that the product meets prospectively defined design and performance specifications." However, no specific quantitative acceptance criteria or their corresponding performance results are detailed in the provided text. The statement is a general declaration of compliance.

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 document. The submission mentions "testing" but does not elaborate on the sample sizes or provenance of any data used for testing.

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 information is not applicable in the context of this device's regulatory pathway. The Stöckert S5 System Gas Blender is an accessory to a heart-lung machine, and its performance evaluation would likely involve engineering and functional testing against specifications, rather than expert-adjudicated ground truth data sets in the way an AI diagnostic device would.

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

This information is not applicable for the reasons stated in question 3.

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

This information is not applicable. The device is not an AI/ML diagnostic or assistive tool, but rather a physical component (gas blender) for a heart-lung machine. Therefore, an MRMC study comparing human reader performance with and without AI assistance is irrelevant.

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

This information is not applicable. The device is a physical hardware component and does not operate as a standalone algorithm.

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

This information is not applicable. For a device like a gas blender, ground truth would typically relate to known physical properties, engineering specifications, and validated measurement techniques, rather than expert-derived medical diagnoses or outcomes data. Its functionality would be verified based on its ability to accurately blend gases according to set parameters.

8. The sample size for the training set

This information is not applicable. This is not an AI/ML device that requires a training set.

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

This information is not applicable. This is not an AI/ML device that requires a training set.

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