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The Hickman® TriFusion™ Triple Lumen Long-Term Central Venous Catheter is indicated for use in attaining short term or long term vascular access for intravenous infusion therapy and blood sampling via the internal jugular vein, external jugular vein, and subclavian vein. All Hickman® TriFusion™ catheters are designed for apheresis, and the administration of I.V. fluids, blood products, drugs, and parenteral nutrition solutions, as well as blood withdrawal. The Hickman® TriFusion™ catheter incorporates three large, equal size lumens appropriate for apheresis procedures.

The Hickman TriFusion™ Catheters are open-ended triple lumen radiopaque polyurethane catheters. The Hickman TriFusion™ Catheters are 12 Fr triple lumen with up to 27 cm insertion length. The Hickman TriFusion™ Catheters have three equal sized lumens with the distal lumen extending beyond the proximal lumens. The proximal end of the TriFusion™ Catheter consists of three luer connectors, occlusion clamps, and priming volume ID tags. Catheters are provided sterile in two kit configurations, an Intermediate Tray and a Microintroducer (MI) Tray.

The provided text describes the 510(k) summary for the Hickman® TriFusion™ Catheter. It outlines the device, its intended use, and substantial equivalence to a predicate device based on nonclinical performance testing. However, it does not contain information about acceptance criteria or a study proving that the device meets specific acceptance criteria in the context of diagnostic performance or AI effectiveness.

The document relates to a medical device (intravascular catheter), not an AI/ML-driven diagnostic device. Therefore, many of the requested categories like "test set," "ground truth," "experts," "adjudication," "MRMC study," and "training set" are not applicable to the information provided.

Based on the provided text, here's what can be answered:

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

The document refers to adherence to recognized standards and in-house protocols rather than specific numerical acceptance criteria for each test with corresponding performance results. It generally states that "test results met the requirements" and "performance data demonstrate equivalence."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
Not applicable. This is a medical device (catheter) submission, not an AI/ML diagnostic. The testing involved nonclinical performance tests of the device itself.

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):
Not applicable. Ground truth in the context of diagnostic imaging or AI is not relevant here. The "ground truth" for the catheter's performance is determined by established engineering and material science standards.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
Not applicable. Adjudication methods are typically used for expert review of images or data in diagnostic studies.

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. This is a submission for a physical medical device (catheter), not an AI-assisted diagnostic tool.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
No. This is a physical medical device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
The "ground truth" for this device's performance is established by recognized international standards (e.g., ISO 10555-1, ISO 10555-3, ISO 594-1, ISO 594-2) and FDA guidance, as well as in-house validation protocols. These standards define acceptable physical, chemical, and mechanical properties.

8. The sample size for the training set:
Not applicable. There is no AI/ML training set for this device.

9. How the ground truth for the training set was established:
Not applicable. There is no AI/ML training set for this device.

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