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The MASC Surgical Mask is intended to be worn to protect both the patient and healthcare personnel from transfer of microorganisms, body fluids and particulate material. This face mask is intended for use in infection control practices to reduce the potential exposure to blood and body fluids. This is a single use, disposable device provided non-sterile.

The MASC Surgical Mask is a three-ply, nonwoven, flat pleated mask. The MASC surgical mask consists of an outer layer (polypropylene spun-bond, blue), a middle filter layer (Polypropylene melt-blown, white), and an inner layer (Polypropylene spun-bond, white). The three layers of the mask body are collated with ultrasonically welded edges and comes in two models, an ear- loop model (NFR03U-01A) and a tie-on model (NFR03U-01B). The polyester spandex blend ear loops or polypropylene tie-on band are ultrasonically bonded to the sides of the non-filtration area of the mask to secure the mask over the user's mouth, nose and chin. A malleable polyethylene coated wire nosepiece is placed within the top binding for comfort and individualized fit over the nose. The MASC Surgical Mask is a single use, disposable, nonsterile device which conforms to ASTM F2100-19 Level 3. This device does not contain any natural rubber latex or fiber glass materials.

This document is a 510(k) Premarket Notification for a surgical mask (MASC Surgical Mask, K211984). It's a regulatory submission to the FDA, not a study report for an AI/ML medical device. Therefore, the information requested about acceptance criteria and study proving device meets acceptance criteria, specifically related to AI/ML devices, cannot be fulfilled from this document.

However, I can extract information regarding performance testing for this physical medical device (surgical mask) as outlined in the provided text.

Here's the relevant information from the document related to the MASC Surgical Mask's performance:

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

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

• Sample Size: For Fluid Resistance, Bacterial Filtration Efficiency, Particulate Filtration Efficiency, Differential Pressure, and Flammability tests, three non-sequential lots of 32 units each were tested, totaling 96 units for each of these performance tests (e.g., "Three non-sequential lots of 32 (total of 96, AQL 4.0) passed at 160mmHg").
• Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective, as these terms are more applicable to clinical studies. These are laboratory-based performance tests of the physical mask.

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 is not applicable to the performance testing of a surgical mask, which relies on standardized physical and biological tests rather than expert interpretation of images or clinical outcomes.

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

Not applicable. The tests are objective measurements according to specified ASTM, EN, and ISO standards.

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 document is for a surgical mask, not an AI/ML medical device.

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

Not applicable. This is not an AI/ML device.

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

The "ground truth" for the surgical mask performance is defined by the acceptance criteria of the established international standards (ASTM, EN, ISO, and CFR) for physical and biological properties. For example, for Bacterial Filtration Efficiency, the "ground truth" is a measured efficiency of $\ge$ 98% when tested according to ASTM F2101.

8. The sample size for the training set

Not applicable. This is not an AI/ML device.

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

Not applicable. This is not an AI/ML device.

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