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The UltimumTM EV Hemostasis Introducers are designed for the introduction of angiographic catheters, closed end catheters, balloon catheters and electrodes into a vessel where minimizing the blood loss is essential.

The UltimumTM EV (14F-22F) Hemostasis Introducers are introducers designed to provide easy access to the vascular system while providing convenient temporary closure of a standard indwelling introducer access site. The introducers include a sheath, hub, hemostasis valve, sideport for 3-way stopcock, radiopaque tip marker, and dilator. The introducers are provided sterile, and are intended for single-use only.

The provided text describes a 510(k) premarket notification for the "Ultimum™ EV Hemostasis Introducer." This is a medical device designed for introducing catheters and electrodes into a vessel while minimizing blood loss. The submission aims to establish substantial equivalence to a predicate device.

However, the document does not contain the detailed information required to answer the questions about acceptance criteria and a study proving device performance in the typical sense of an AI/ML device study.

Here's why and what information we can extract:

• Device Type: This is a physical medical device (catheter introducer), not an AI/ML software device. Therefore, the questions about "test set," "training set," "ground truth," "number of experts," "adjudication," and "MRMC comparative effectiveness study" are not applicable in their usual context for AI/ML.
• Study Type: The submission focuses on confirmatory testing to demonstrate substantial equivalence to an existing predicate device, not on clinical effectiveness studies with human readers or AI algorithms.

Given this, I will answer the questions based on the type of information typically provided for this kind of physical device submission where applicable, and explicitly state when the requested information is not present or not relevant to this type of device.

Acceptance Criteria and Study Proving Device Meets Acceptance Criteria

1. A table of acceptance criteria and the reported device performance:

The document states that "Confirmatory testing included tensile testing, flexure testing, hemostasis seal testing and visibility testing comparing competitor devices." It also explicitly mentions that "All technological characteristics of the Ultimum™ EV Hemostasis Introducers are substantially equivalent to the predicate device including product design, packaging, sterilization, and labeling."

While specific numerical acceptance criteria (e.g., "hemostasis seal must withstand X pressure") and precise reported performance metrics are not provided in this 510(k) summary, the overall acceptance criterion is:

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

• Sample Size: The document does not specify the exact sample sizes (e.g., number of units tested) for the tensile, flexure, hemostasis seal, or visibility testing.
• Data Provenance: Not explicitly stated, but given it's a submission by St. Jude Medical, Daig Division, based in Minnetonka, Minnesota, U.S.A., it's highly probable the testing was conducted in the U.S. and is prospective for the purpose of this submission (i.e., tests were conducted for this submission, rather than re-analyzing existing data).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

This question is not applicable to this type of device and study. "Ground truth" in the context of physical device testing refers to the objective physical measurements obtained from the confirmatory tests (tensile, flexure, hemostasis seal, visibility). These are not established by human expert consensus or interpretation in the way an AI/ML diagnostic device's ground truth would be.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

This question is not applicable. Adjudication methods like 2+1 or 3+1 are used for resolving disagreements in expert interpretations, typically for diagnostic image review. For physical device testing, physical measurements are largely objective and do not require this type of 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 question is not applicable. The device is a physical catheter introducer, not an AI-assisted diagnostic tool. Therefore, no MRMC study or assessment of human reader improvement with AI assistance would have been conducted or is relevant.

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

This question is not applicable. This is a physical medical device, not an algorithm, so a standalone performance assessment of an algorithm is irrelevant.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc):

The "ground truth" for the confirmatory testing (tensile, flexure, hemostasis seal, visibility) would be the objective physical measurements and engineering specifications obtained from standardized testing protocols. These are not derived from expert consensus, pathology, or outcomes data.

8. The sample size for the training set:

This question is not applicable. This is a physical medical device, not an AI/ML algorithm. There is no "training set" in the context of this 510(k) submission. Design and manufacturing processes are refined through product development, but this is not typically referred to as a "training set" in the AI sense.

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

This question is not applicable for the reasons stated above.

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