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DeRoyal Surgical Gowns are identified in 21 CFR, part 878.4040 as surgical attire to be worn by operating room personnel during surgical procedures to protect both operating room personnel and the patient from the transfer of microorganisms, body fluids and other particulate materials.

The DeRoyal Surgical Gowns are comprised of Polypropylene Spunbond Meltblown Spunbond (SMS) material commonly used in the industry for these devices have been tested according to relevant performance standards (ISO 10993, AATCC 127, AATCC42, ASTM1671, 16 CFR 1610, ASTM 1342, ASTM D1424, ASTM D5034, NFPA Standards) to determine their equivalency to the predicate devices.

The provided document is a 510(k) premarket notification for DeRoyal Surgical Gowns. It states that the device is substantially equivalent to legally marketed predicate devices based on a comparison of technological characteristics and performance standards. However, it does not contain a study that proves the device meets specific acceptance criteria in the way a clinical trial or algorithm performance study would.

This document is for a medical device (surgical gowns), which are evaluated based on material properties, performance standards, and comparison to legally marketed predicate devices, rather than a "study proving device meets acceptance criteria" in the context of an AI/ML device or a device requiring a clinical efficacy trial.

Therefore, many of the requested fields cannot be directly extracted from this document, as they pertain to a different type of device evaluation (e.g., diagnostic device, software as a medical device).

Here's an attempt to answer the questions based only on the provided text, while acknowledging its limitations for this type of query:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly list specific "acceptance criteria" for performance values in a table format that are then compared to reported device performance. Instead, it states that the device materials "have been tested according to relevant performance standards (ISO 10993, AATCC 127, AATCC42, ASTM1671, 16 CFR 1610, ASTM 1342, ASTM D1424, ASTM D5034, NFPA Standards) to determine their equivalency to the predicate devices."

The "device performance" is implicitly that it meets the requirements of these standards and is equivalent to the predicate devices. The specific numeric results of these tests are not provided in this summary.

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

This information is not provided in the document. For material testing, sample sizes would depend on the specific ASTM, ISO, or AATCC standards, but these details are not disclosed in this 510(k) summary. The concept of "data provenance" (country, retrospective/prospective) is not applicable here as it relates to clinical data, not material performance testing.

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

This is not applicable to the evaluation of surgical gown material properties. "Ground truth" in this context would be established by standardized laboratory testing methods, not expert consensus on medical images or diagnoses.

4. Adjudication Method for the Test Set

Not applicable. Adjudication methods (e.g., 2+1, 3+1) are used for clinical trial data or expert review scenarios, not for material property testing according to established international standards.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Not applicable. This is not a diagnostic device or an AI-assisted tool where human reader performance would be measured or improved.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) Performance

Not applicable. Surgical gowns are physical medical devices, not algorithms.

7. Type of Ground Truth Used

The "ground truth" for the device's properties is established through standardized laboratory testing according to the listed international and national consensus standards (ISO, AATCC, ASTM, NFPA, 16 CFR).

8. Sample Size for the Training Set

Not applicable. Surgical gowns do not involve machine learning algorithms with a training set.

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

Not applicable.

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