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Medi-Tech International Corporations Easy-View Mitts are intended to be utilized as a patient safety device when patients are assessed at risk of disrupting life-saving treatments or could cause self-injury.

The Medi-Tech International Corp. Protective Restraint is a protective restraint device which is intended for medical purposes to control the movement of a patient's hand or finger movements thereby enabling examination or protection of the patient or others. Device Design Materials Used/Physical Properties: The Medi-Tech Protective Restraint is designed similar to those marketed by other manufacturers. Primarily comprised of cotton and polyester material and polyester fiber filling.

The Medi-Tech International Corp. Protective Restraint (Easy-View Mitts, Model #MTRM281) is a protective restraint device intended to control the movement of a patient's hand or finger movements to enable examination or protection of the patient or others, particularly when patients are at risk of disrupting life-saving treatments or causing self-injury.

Here's an analysis of the acceptance criteria and study:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size:
  • Non-Clinical Data: One individual, Marilyn Geiger, Medi-Tech International Corporation's Product Manager, was used for the "Non-Clinical Data" tests (Safety, Effectiveness, Function).
  • Bench Testing: The document does not specify the number of units or samples tested for each of the bench tests (e.g., how many fabric swatches for tearing resistance, how many mitts for seam failure). The tests were performed by Intertek testing facility.
• Data Provenance: Retrospective, as these tests were conducted to demonstrate substantial equivalence to an already marketed device (J.T. Posey Company Protective Restraint K963413). The document doesn't specify country of origin for the data beyond the testing facility (Intertek) being utilized.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Non-Clinical Data: One "expert" (Marilyn Geiger, Product Manager) conducted the tests and reported the results. Her specific qualifications beyond being the company's Product Manager are not detailed in the provided text. No independent expert consensus was explicitly stated.
• Bench Testing: The testing was performed by Intertek, a third-party testing facility. While not explicitly stated as "experts," Intertek is a recognized testing institution, implying their personnel are qualified to conduct the specified ASTM and AATCC tests. No ground truth was established by human consensus for these physical tests; they are objective measurements against established standards.

4. Adjudication method for the test set

• Non-Clinical Data: No explicit adjudication method (like 2+1 or 3+1) is mentioned. The testing appears to have been observed and reported by a single individual (Marilyn Geiger).
• Bench Testing: The concept of "adjudication" as typically applied to clinical or image-based studies doesn't apply directly here. The bench tests involve objective measurements and visual inspections against defined criteria and standards. The results would be objectively determined by the testing equipment and qualified technicians at Intertek.

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

• No MRMC comparative effectiveness study was done. This device is a physical protective restraint and not an AI-assisted diagnostic or therapeutic tool. Therefore, the concept of human readers improving with or without AI assistance is not applicable.

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

• No standalone algorithm performance study was done, as this is a physical medical device, not a software algorithm or AI.

7. The type of ground truth used

• Non-Clinical Data (Safety, Effectiveness, Function): The ground truth was based on observational assessment and comparison to the predicate device by the Product Manager. The "ground truth" was essentially the subjective judgment that the device performed "as intended" and "similar" to the predicate.
• Bench Testing: The ground truth was based on objective physical measurements against established industry standards (e.g., ASTM, AATCC) and visual inspections for compliance with design specifications (e.g., labeling, workmanship, sharp edges). The predicate device served as a comparative benchmark for several functional and material properties (e.g., strap strength, material composition).

8. The sample size for the training set

• No specific training set in the context of machine learning or AI is mentioned, as this is a physical medical device. The "training" for such a device would typically involve design, engineering, and manufacturing processes, not data-driven algorithm training.

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

• Not applicable, as there is no training set in the context of AI/ML for this device. The design and development of the device would have been guided by engineering principles, material science, and regulatory requirements, and benchmarked against existing devices.

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