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The Medline Level 4 Surgical Gown with Breathable Sleeves is a sterile, single use surgical apparel intended to be worn by healthcare professionals to help protect both the patient and the healthcare worker from the transfer of microorganisms, body fluids, and particulate matter. The Medline Level 4 Surgical Gown with Breathable Sleeves meets the Level 4 requirements of ANSI/AAMIPB70:2022 Liquid barrier performance and classification of protective apparel and drapes intended for use in healthcare facilities.

The Medline Level 4 Surgical Gown with Breathable Sleeves is also sold in a non-sterile version to repackagers/kitting entities, to be sterilized using the validated ethylene oxide sterilization method according to ISO 11135-1 in its final finished form, as a kit component.

The Medline Level 4 Surgical Gown with Breathable Sleeves is a single-use, sterile, disposable medical device that is provided in a variety of sizes (Large, X-Large, 3X-Large, 3X-Large and X-Long (only Sirus)) and styles (Sirus, Aurora and Eclipse). The Medline Level 4 Surgical Gown with Breathable Sleeves is constructed from nonwoven SMS (spunbond, meltblown, spunbond), a poly-fabric reinforcement on the chest front panel, and a breathable film (Polyester ether and polyester) for the sleeves. Each gown is designed with a hook and loop closure at the neck, ties at the waist, knitted cuffs at the end of the sleeves as well as a transfer tab for ease in donning. The Medline Level 4 Surgical Gown with Breathable Sleeves has been tested according to ANSI/AAMI PB70:2022 and meets AAMI Level 4 barrier level protection for a surgical gown.

This document describes the performance testing for the Medline Level 4 Surgical Gown with Breathable Sleeves (K242844), a surgical apparel device. The information provided is based on non-clinical testing only, as no clinical testing was performed for this 510(k) submission.

Here's an analysis of the acceptance criteria and the study proving the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document describes non-clinical tests conducted to verify design specifications. For each test listed in the table, the "Results" column indicates a "Pass," meaning the acceptance criteria were met.

• Sample Size: The specific sample sizes for each non-clinical test (e.g., number of fabric specimens tested for ASTM F1671) are not detailed in this summary. The 510(k) summary typically provides a high-level overview, and the detailed test reports (which FDA reviews) would contain this information.
• Data Provenance: The tests are generally performed by accredited testing laboratories or the manufacturer's own qualified labs. The document does not specify the country of origin of the data. Given it's an FDA submission, the tests would typically adhere to recognized international standards like ISO and ASTM. These are prospective tests performed specifically to support the regulatory submission of this new device.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• Not Applicable. For this type of medical device (surgical gowns), "ground truth" is established through adherence to standardized, objective performance tests (e.g., measuring water resistance, breaking strength, biocompatibility). These are not studies that rely on expert interpretation of images or clinical outcomes to establish ground truth in the way an AI diagnostic device would.

4. Adjudication Method for the Test Set:

• None. Adjudication methods (like 2+1, 3+1) are relevant for studies involving human interpretation or clinical endpoints, often in the context of diagnostic accuracy or clinical trials. As this is a non-clinical performance evaluation against objective standards, no adjudication method is used. The results are quantitative measurements interpreted against pre-defined thresholds.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done:

• No, an MRMC comparative effectiveness study was not done. This type of study is typically performed for diagnostic devices (especially those involving image interpretation by human readers) to assess how an AI algorithm impacts reader performance. The Medline Level 4 Surgical Gown is a protective apparel, not a diagnostic device, and its performance is evaluated through physical and material property tests, not human reader studies.

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

• Not Applicable. This question typically pertains to AI/ML software as a medical device (SaMD). The Medline Level 4 Surgical Gown is a physical medical device. Its performance is inherent in its material properties and manufacturing, not an algorithm.

7. The Type of Ground Truth Used:

The "ground truth" for this device's performance is established by objective, quantitative measurements against published national and international standards. This includes:

• Biocompatibility Standards: ISO 10993 series (Cytotoxicity, Irritation, Sensitization).
• Barrier Performance Standards: ASTM F1671 (blood-borne pathogens), AATCC 42 (impact penetration), AATCC 127 (hydrostatic pressure), and ANSI/AAMI PB70:2022 (overall liquid barrier performance classification).
• Material Strength Standards: ASTM D5034 (breaking strength), ASTM D5587 (tearing strength), ASTM D1683 (sewn seam failure).
• Other Performance Standards: 16 CFR 1610 (flammability), ASTM E96 (water vapor transmission/breathability).

The "ground truth" is therefore the measured physical and chemical properties of the gown, compared against the predefined acceptance criteria of these standard test methods.

8. The Sample Size for the Training Set:

• Not Applicable. The concept of a "training set" is relevant for machine learning algorithms. This device is a manufactured product, not an AI algorithm. Its design and manufacturing process would involve material selection and engineering, not machine learning training.

9. How the Ground Truth for the Training Set Was Established:

• Not Applicable. As there is no training set for an AI algorithm, this question is not relevant.

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