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To be used in nitrous oxide/oxygen sedation systems for the delivery to a patient a mixture of nitrous oxide and oxygen gases, for the removal from the treatment location of gases expired by the patient, and removal from the treatment location any excess gases delivered to a patient.

The RFS Vacuum Gauge Scavenging Circuit is designed for the removal of patient expired and excess gases during nitrous oxide / oxygen sedation procedures. For proper operation, the scavenging circuit must be connected to a vacuum pump capable of providing at least 45 LPM of scavenging on an operating vacuum pressure of 8 in Hg. The vacuum pump should vent exhaust gases to the outside of the building, away from fresh air intakes, windows, or walkways.

The scavenging circuit consists of the scavenging hub, which connects to a nasal hood, exhaust tubing and connectors; and the RFS Vacuum Gauge. The nasal hood fits over the patient's nasal cavity, which serves to deliver medical gases to the patient and which allows removal of expired, excess, and unused medical gases from the nasal hood and surrounding areas.

This device measures pressure, not flow. Setting the float ball at the yellow indicator level This device measures pressure. This will result in the NIOSH recommended 45 LPM provided the mask is correctly attached, and there are no kinks or obstructions in the flow path.

The provided text describes a 510(k) summary for the "RFS Vacuum Gauge Scavenging Circuit." This document is a premarket notification for a medical device and typically focuses on demonstrating substantial equivalence to a predicate device, rather than presenting a detailed study with acceptance criteria and performance metrics in the way a clinical trial report would for an AI/ML device.

Therefore, much of the requested information (acceptance criteria table, sample size, expert qualifications, adjudication, MRMC study, standalone performance, training set details) is not applicable or not provided in this type of regulatory submission for a physical scavenging circuit.

However, based on the provided text, we can infer some aspects related to the device's intended performance and its substantiation.

Here's an analysis based on the provided text:

Acceptance Criteria and Study Details

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

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

• Not Applicable / Not Provided. This document does not describe a test set or data provenance in the context of validating a diagnostic or AI device's performance through data analysis. It's a submission for a physical device, and performance is likely demonstrated through engineering verification and validation testing, not a clinical data-driven "test set."

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

• Not Applicable / Not Provided. Ground truth establishment by experts is not described for this type of device. Performance is assessed against engineering specifications and regulatory standards.

4. Adjudication method for the test set

• Not Applicable / Not Provided. Adjudication methods are not described for this type of device.

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 a physical medical device (scavenging circuit), not an AI-assisted diagnostic tool. Therefore, an MRMC study related to human reader performance with AI assistance is not relevant or mentioned.

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

• Not Applicable. This is a physical device, not an algorithm.

7. The type of ground truth used

• Engineering Specifications and Industry Standards: The "ground truth" for this device would be its ability to meet established engineering performance specifications (e.g., vacuum pressure, flow rates, sealing capabilities) and adherence to industry standards, such as the NIOSH recommended 45 LPM scavenging. This is determined through physical testing and measurement, not typically against expert consensus, pathology, or outcomes data in the way an AI diagnostic device would.

8. The sample size for the training set

• Not Applicable / Not Provided. This is a physical device, not a machine learning model, so there is no "training set."

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

• Not Applicable / Not Provided. As there is no training set, this question is not relevant.

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