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The DeRoyal Industries, Inc. Poncho Vest Restraint is indicated for use, at the discretion of a responsible licensed healthcare professional, to limit movements thereby enabling examination or protection of the patient or others. The Poncho Vest Restraint is a protective restraint that is intended to be used with a bed or wheelchair.

The DeRoyal Industries, Inc. Poncho Vest Restraint device that is intended for medical purposes to limit movements thereby enabling examination or protection of the patient or others.
Device Design/Materials Used/Physical Properties: The DeRoyal Poncho Vest Restraint is designed similar to those marketed by other manufacturers. The Poncho Restraint is primarily comprised of cool knit or cotton with polyester straps. The fasteners are made of a nylon hook and loop or steel nickel plated buckles.

This document describes a medical device called the "DeRoyal Industries, Inc. Poncho Vest Restraint," which is a physical restraint. The provided text is a 510(k) summary for this device, seeking clearance from the FDA.

Based on the information provided, this is not a device that involves artificial intelligence (AI), machine learning (ML), or any form of digital diagnostics/analysis. Therefore, the concepts of acceptance criteria related to algorithmic performance, test sets, ground truth establishment, expert readers, MRMC studies, or standalone algorithm performance are not applicable here.

The document discusses equivalence to predicate devices based on:

• Materials: "Principally comprised of cool knit or cotton with polyester straps. The fasteners are made of a nylon hook and loop or steel nickel plated buckles."
• Design: "designed similar to those marketed by other manufacturers."
• Intended Use: "to limit movements thereby enabling examination or protection of the patient or others."

To provide an answer that aligns with the spirit of your request (even though AI/ML is not involved), I will reinterpret the "acceptance criteria" and "study" in the context of a traditional medical device submission like this.

This medical device is a physical restraint and thus the concepts of "device performance" in the context of AI/ML metrics (like accuracy, sensitivity, specificity, AUC) and associated studies (like MRMC, standalone performance) are not applicable. The documentation focuses on demonstrating substantial equivalence to predicate devices based on design, materials, and intended use.

Here's an interpretation based on the provided text, using the structure you requested, even though it deviates significantly from an AI/ML context:

1. Table of "Acceptance Criteria" and the Reported "Device Performance"

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

• Sample Size: Not applicable. This is not a study involving a "test set" of data in the AI/ML sense. The "test" is a comparison to existing, legally marketed predicate devices.
• Data Provenance: Not applicable. No specific data provenance (country, retrospective/prospective) is involved in this type of comparison for a physical restraint.

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

• Not applicable. Ground truth for a physical restraint is typically established through engineering design principles, material testing (not detailed in this summary), and clinical experience with predicate devices, rather than expert consensus on diagnostic data.

4. Adjudication Method for the Test Set

• Not applicable. There is no "test set" requiring adjudication in the context of this device.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done

• No. MRMC studies are used for diagnostic accuracy and reader performance, which are not relevant for a physical restraint device of this nature.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

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

7. The Type of Ground Truth Used

• In this context, the "ground truth" for demonstrating safety and effectiveness would be established through:
  • Predicate Device Performance: The known performance and safety profiles of the legally marketed predicate devices.
  • Engineering Standards/Material Specifications: Adherence to established standards for textiles, fasteners, and overall design integrity (though specific standards are not detailed in this summary).
  • Clinical Experience: The general understanding and experience of healthcare professionals regarding the safe and effective use of patient restraints.

8. The Sample Size for the Training Set

• Not applicable. There is no "training set" for an AI/ML model.

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

• Not applicable. There is no "training set" or AI/ML model for which ground truth would be established in this manner.

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