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This mask is intended to provide an interface for application of CPAP or bi-level therapy to patients. The mask is for single patient use in the home or multi-patient use in the hospital/institutional environment. The mask is to be used on patients (>66lbs/30kg) for whom CPAP or bi-level therapy has been prescribed.

The Shimmer Full Face Mask is intended to provide an interface for application of CPAP or bi-level therapy to patients. The mask is for single patient use in the home or multi-patient use in the hospital/institutional environment. The mask is to be used on patients (>66lbs/30kg) for whom CPAP or bi-level therapy has been prescribed.

The Shimmer Full Face Mask is intended to be used with positive airway pressure devices such as CPAP or bi-level systems. Like the predicate device, Revolution Full Face Mask (K082866), this mask covers the patients' nose and mouth, and is secured using a 4-point headgear. The mask seal directs positive pressure into the patient's nose and mouth.

The Shimmer Full Face Mask incorporated the anti-asphyxia feature into the cushion design, as opposed to the traditional full face mask anti-asphyxia feature typically located in the mask elbow component. Refer to Figure 3.

The anti-asphyxia feature incorporated into the Shimmer full face cushion is called a fresh air inlet valve (FAIV), and it is designed to work differently than other antiasphyxia valves. A traditional anti-asphyxia valve opens to atmosphere during a single fault condition, which routes inspiratory and expiratory airflow through the same open to atmosphere hole, while occluding the patient connection port. The FAIV, in a single fault condition, opens to atmosphere during inspiratory airflow and it closes to atmosphere during expiratory airflow. Since the patient connection port is no longer occluded during single fault condition, air will exhaust through the patient connection port and mask exhalation features.

The provided document is limited to a 510(k) summary for a medical device (Shimmer Full Face Mask) and primarily focuses on demonstrating substantial equivalence to a predicate device. It briefly mentions design verification tests and compliance with general safety and effectiveness standards. However, it does not contain the detailed information required to answer many of your specific questions about the acceptance criteria, the study details (like sample size for test sets, data provenance, expert qualifications, adjudication methods), MRMC studies, or standalone algorithm performance.

The document indicates that the device is a physical mask, not an AI/software device, which explains the absence of data related to AI/algorithm performance.

Here's a breakdown of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

The document mentions that "All tests were verified to meet the required acceptance criteria," but it does not explicitly list the acceptance criteria or the specific reported device performance values for each test. It only lists the types of non-clinical tests performed.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not specified in the provided document. The tests performed are primarily engineering/performance tests on the device itself, not studies involving patient data in the typical sense of a clinical trial.
• Data Provenance (country of origin, retrospective/prospective): Not applicable for the types of tests described (physical device performance testing).

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

• This is not applicable as the document describes performance testing of a physical medical device (mask), not an AI/software device that requires expert-established ground truth for a test set. The "ground truth" here would be the physical properties and performance characteristics of the mask as measured in a lab setting.

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

• This is not applicable for the physical device performance tests described. Adjudication methods are typically relevant for human review of data, especially in AI/imaging studies.

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, an MRMC comparative effectiveness study was not done. This is a physical respiratory mask, not an AI or imaging device with human readers.

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

• No, a standalone algorithm performance test was not done. This device is a physical mask, not an algorithm.

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

• The "ground truth" for the device's performance is established by engineering specifications, industry standards (e.g., ISO), and physical measurements/testing rather than clinical outcomes or expert consensus. For example, "Pressure Drop" would have a specified maximum limit, and the measured drop would be compared to that limit.

8. The sample size for the training set

• Not applicable. This device is a physical product and does not involve AI or machine learning models that require a "training set."

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

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

In summary: The provided document details a 510(k) submission for a non-continuous ventilator mask. The information focuses on showing substantial equivalence through design and non-clinical performance testing against established standards and a predicate device. It does not involve AI, software, or clinical studies with patient data adjudicated by experts, which are the typical contexts for the questions you've asked.

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