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The following Crosstex® Isofluid Plus Surgical Masks are intended for use in infection control practices to minimize contamination caused by inhaled and exhaled microorganisms and reduce the potential exposure of the wearer to blood and body fluids. (non-sterile)

• Crosstex® Isofluid Plus Surgical Earloop Face Mask Blue .
• Crosstex® Isofluid Plus FogFree Surgical Earloop Face Mask Blue .
• Crosstex® Isofluid Plus FogFree Surgical Earloop Face Mask with Splash Visor Blue .

The Crosstex Isofluid Plus Surgical Masks are constructed of a cellulose inner facing, a 100% Spundonded porypropyitent elastic loops. The nose piece for the polypropyiene inter firedia, with non facil cae no fog strip (if applicable) is made of melt blow polypropylene.

The provided text describes the acceptance criteria and performance of Crosstex® Isofluid Plus Surgical Masks, focusing on non-clinical tests to demonstrate substantial equivalence to a predicate device.

Here's the information extracted and organized:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The "Acceptance Criteria" column is derived from the reported performance of the predicate device, as the submission states that the new device meets "all relevant requirements of the aforementioned test standards" and is "substantially equivalent." Typically, substantial equivalence is established by meeting or exceeding the performance of the predicate device for relevant parameters.

2. Sample Size Used for the Test Set and Data Provenance

The document does not explicitly state a separate "test set" in the context of a dataset for an AI/ML device. Instead, the performance evaluation is based on bench testing of the physical masks.

• Fluid Resistance (ASTM 1862): "32 of 32 pass at 80mm Hg" for the new device, suggesting a sample size of 32 units tested. The predicate device had "30 of 32 pass at 80mm Hg," also implying 32 units tested for the predicate.
• Other tests (PFE, BFE, Flammability, Delta-P): Specific sample sizes are not explicitly stated for these tests within the provided text, but they would typically involve multiple samples to ensure statistical validity.
• Data Provenance: The data provenance is from non-clinical laboratory bench testing conducted to evaluate the physical and performance characteristics of the surgical masks. There is no information about the country of origin of the data beyond the submitter's identification in New York, USA. The nature of these tests makes them prospective in the sense that they are performed on manufactured products for regulatory submission.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not applicable to this submission. The device is a physical surgical face mask, and its performance is evaluated through standardized bench tests (e.g., fluid resistance, particulate filtration). There is no "ground truth" in the context of expert consensus, pathology, or clinical outcomes that would require human experts to interpret data for the test set.

4. Adjudication Method for the Test Set

This information is not applicable as the evaluation relies on objective, standardized physical performance tests rather than subjective human interpretation requiring adjudication.

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. This submission is for a physical medical device (surgical face mask), not an AI/ML-driven diagnostic or image analysis system. Therefore, an MRMC study and the concept of "human readers improving with AI" are irrelevant to this device.

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

This information is not applicable. This is a physical product, not an algorithm.

7. The Type of Ground Truth Used

The "ground truth" for the performance evaluation of these surgical masks is established by standardized laboratory test methods (e.g., ASTM 1862 for fluid resistance, ASTM F2299 for particulate filtration, ASTM F2101 for bacterial filtration, 16CFR1610 for flammability, and Mil M36954C for Delta-P). These methods define objective criteria and measurements to determine the performance against established industry standards and the predicate device's performance.

8. The Sample Size for the Training Set

This information is not applicable. There is no "training set" as this is a physical device, not an AI/ML algorithm.

9. How the Ground Truth for the Training Set was Established

This information is not applicable. As there is no training set, there is no corresponding ground truth for it.

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