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Turbo-Ject Peripherally Inserted Central Venous Catheter (PICC) Sets and Trays are intended for shortor long-term use for venous pressure monitoring, blood sampling, administration of drugs and fluids, and for use with power injectors for delivery of contrast in CT studies. The Turbo-Ject PICC is indicated for multiple injections of contrast media through a power injector. The maximum pressure limit setting for Power Injectors used with the Turbo-Ject PICC may not exceed 325 psi and the flow rate may not exceed the maximum flow rate indicated.

The subject Turbo-Ject® PICCs are radiopaque polyurethane peripherally inserted central venous catheters for short- or long-term use, and can be inserted through a Peel-Away® introducer, or over-thewire. The subject devices are minimally tapered 5.0 Fr single and double lumen catheters. The set components may include the PICC. obturator. Peel-Away® introducer, entry needles, wire guide, and other convenience components. The set is supplied sterile and is intended for one-time use.

The provided text (K132883) describes the testing and acceptance criteria for the "Turbo-Ject® Peripherally Inserted Central Venous Catheter Set". This is a medical device, and the testing described here relates to its mechanical performance and safety, not an AI/ML-driven diagnostic or image analysis device. As such, many of the requested categories (e.g., number of experts, adjudication method, MRMC study, training set ground truth) are not applicable to this type of device and study.

However, I can extract the relevant information regarding the acceptance criteria and the performance studies as described in the document.

Summary of Device Acceptance Criteria and Performance Study for a Medical Device (Turbo-Ject® PICC Set):

The device is a non-AI/ML medical device (a catheter set). The acceptance criteria and testing focus on physical and mechanical performance to ensure safety and effectiveness, not diagnostic accuracy or human-AI interaction.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• The document does not specify the exact sample sizes (number of units tested) for each mechanical test.
• The data provenance is not explicitly stated in terms of country of origin for the data or whether it was retrospective or prospective. These are typically laboratory-based engineering tests conducted by the manufacturer (Cook Incorporated, U.S.A.).

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

• Not Applicable. This is a mechanical device, not one requiring expert human interpretation for ground truth. Ground truth for these tests is established by physical measurement and engineering standards (e.g., ISO standards, pressure gauges, leakage detection methods).

4. Adjudication Method for the Test Set:

• Not Applicable. As mentioned above, these are objective mechanical tests, not subjective assessments requiring adjudication.

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

• No. An MRMC study is relevant for AI/ML diagnostic devices where human readers interpret medical images. This device is a catheter set, and its performance is assessed through laboratory mechanical testing.

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

• Not Applicable. This is a physical medical device, not an algorithm. The "standalone" performance is the mechanical test results themselves.

7. The Type of Ground Truth Used:

• The ground truth for these tests is based on engineering standards, physical measurements, and objective pass/fail criteria derived from the device's intended physical performance and safety requirements (e.g., a certain tensile strength, absence of leakage, ability to withstand pressure).

8. The Sample Size for the Training Set:

• Not Applicable. This device development did not involve a "training set" in the context of machine learning. The design and manufacturing process would involve internal development and validation, but not a distinct machine learning training set.

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

• Not Applicable. See point 8.

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