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The Weinmann Silentflow 2 Exhalation System is intended for prescription use to be used with nasal masks which are used in CPAP or bi-level pressure therapy for adult patients (> 30 kg) and do not have a built-in exhalation device.

The Weinmann Silentflow 2 Exhalation System consists of two components assembled together. It allows for a patient to exhale during CPAP or bi-level pressure therapy.

The provided text describes a 510(k) premarket notification for the Weinmann Silentflow 2 Exhalation System. It does not contain information about acceptance criteria, device performance metrics, or a study that evaluates a device based on such criteria. Instead, it focuses on demonstrating substantial equivalence to predicate devices through functional and environmental testing.

Therefore, most of the requested information cannot be extracted from the given text.

Here's a breakdown of what can and cannot be answered:

1. A table of acceptance criteria and the reported device performance: This information is not explicitly provided in the document. The text states "Functional and environmental testing was conducted to ensure that the device would perform as described in its specifications," but no specific criteria or performance numbers are given.
2. Sample size used for the test set and the data provenance: This information is not provided.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: This information is not provided.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: This information is not provided.
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 is not applicable. The device is an exhalation system, not an AI-assisted diagnostic tool.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: This is not applicable as it's not an algorithm-based device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): This information is not provided. The testing mentioned is "functional and environmental," implying engineering or physical performance standards, rather than medical ground truth as typically understood in diagnostic studies.
8. The sample size for the training set: This is not applicable as there is no mention of a training set for an algorithm.
9. How the ground truth for the training set was established: This is not applicable as there is no mention of a training set for an algorithm.

Summary of available information related to testing:

The device underwent:

• Functional testing: To ensure it performs as described in its specifications.
• Environmental testing: To ensure it performs as described in its specifications.

The conclusion drawn from this testing was that the device is "substantially equivalent to legally marketed predicate devices and is safe and effective for its intended use." However, specific acceptance criteria and reported numerical performance data are not detailed in this document.

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The Weinmann SOMNOmask is intended for prescription use to be used during nasal CPAP or Bi-level Positive Pressure therapy for adult patients (>30 KG).

The Weinmann SOMNOmask comes in three sizes, small, medium and large. It has a removable mask seal. Because the Weinmann SOMNOmask does not contain any ports or vents for removing the CO2 buildup, an external exhalation device must be used. The SOMNOmask is secured to the patient's head with a 4-point headgear called the SOMNOstrap.

This document (K013738) describes a 510(k) premarket notification for the Weinmann SOMNOmask, a nasal mask intended for use during CPAP or Bi-level Positive Pressure therapy. The submission focuses on demonstrating substantial equivalence to predicate devices, rather than establishing specific performance acceptance criteria through clinical studies.

Based on the provided information, the following answers are generated:

1. Table of Acceptance Criteria and Reported Device Performance:

The provided 510(k) summary does not specify quantifiable acceptance criteria for clinical performance in a table format. The study described focuses on technical characteristics and safety, rather than effectiveness metrics against pre-defined thresholds.

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

• Sample Size for Test Set: Not applicable. This submission does not describe a clinical performance test set with human subjects or a dataset for algorithm evaluation. The "testing" referred to is laboratory-based technical testing of the physical mask.
• Data Provenance: Not applicable. The "testing" mentioned is technical testing of the device itself (drop test, temperature, flow resistance) rather than data from human subjects.

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

Not applicable. No ground truth establishment by experts is mentioned as part of this 510(k) submission, as it focuses on technical equivalence and laboratory testing, not clinical performance or diagnostic accuracy.

4. Adjudication method for the test set:

Not applicable. No adjudication method is mentioned as part of this 510(k) submission, as it does not involve a clinical performance test set or data requiring expert review.

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:

Not applicable. This device is a physical medical device (nasal mask), not an AI-powered diagnostic or assistive tool. Therefore, an MRMC study comparing human reader performance with or without AI assistance is not relevant or described.

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

Not applicable. This device is a physical medical device, not an algorithm.

7. The type of ground truth used:

Not applicable. The relevant "truth" in this submission relates to the device meeting its technical specifications and being substantially equivalent to predicate devices, rather than ground truth for diagnostic accuracy or clinical outcomes.

8. The sample size for the training set:

Not applicable. This submission does not describe a machine learning algorithm or a training set.

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

Not applicable. This submission does not describe a machine learning algorithm or a training set.

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