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The SUPERA VERITAS® Interwoven Self-Expanding Nitinol Stent Transhepatic Biliary System is indicated for palliative treatment of biliary strictures produced by malignant neoplasms.

The SUPERA VERITAS® Interwoven Self-Expanding Nitinol Stent Transhepatic Biliary System is a 7Fr, 0.014" or 0.018" guidewire compatible, multi-lumen sheath based delivery system comprised of a Handle and a SUPERA® Biliary Stent. The stent delivery catheter includes a radiopaque Stent Length Marker Band and Distal Sheath Marker Band embedded in the Outer Sheath to aid in proper stent positioning; a Thumb Slide connected internally for advancing the Stent out of the Outer Sheath while the Outer Sheath moves proximally in a de-coupled fashion; a Sheath Flush Port for flushing the central lumen of the device: a Deployment Lock that when actuated enables the final deployment stroke of the stent; a Guidewire Lumen with a radiopaque Catheter Tip located on the distal end of the Catheter Shaft; a Guidewire Flush Port used for flushing the Guidewire Lumen; a Stent Driver which deploys the stent distally relative to the Outer Sheath; and the System. Lock which eliminates the possibility. of premature deployment. The working length of the delivery catheter is 120cm.

The SUPERA® stent housed within the 120cm SUPERA VERITAS® stent delivery catheter is a closed end interwoven self-expanding Nitinol stent. The SUPERA stent is composed of 6 interwoven, closed loop Nitinol wires. The wire loops are closed via a proprietary welding process which utilizes small Nitinol tubes that act as a coupler to provide the mechanical means of joining the wire ends.

Here's a breakdown of the acceptance criteria and the study information for the SUPERA VERITAS® Interwoven Self-Expanding Nitinol Stent Transhepatic Biliary System, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document mentions "Verification Bench-top Testing" and that this testing ensured the device met product specifications. However, specific sample sizes for each test are not provided in the document. The data provenance is from bench-top testing, not clinical data, and is therefore neither retrospective nor prospective in the typical clinical sense.

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

This information is not provided in the document. The testing described is physical, bench-top testing of the device's mechanical properties, not an evaluation requiring expert interpretation of diagnostic output.

4. Adjudication Method for the Test Set

This information is not applicable or not provided as the testing is bench-top performance verification, not an evaluation that typically involves adjudication of results by multiple human observers.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of how much human readers improve with AI vs without AI assistance

No MRMC study was performed or is mentioned. This device is a medical stent, not an AI diagnostic tool. Therefore, the concept of human readers improving with or without AI assistance is not relevant to this submission.

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

No standalone algorithm performance study was done. This device is a physical medical device (stent delivery system), not a software algorithm.

7. The Type of Ground Truth Used

The ground truth for the performance testing ("Verification Bench-top Testing") was product specifications and requirements. Each test (e.g., Bond Tensile Strength, Dimensional Characterization) would have had a predefined acceptable range or threshold set by the manufacturer against which the device's performance was measured.

8. The Sample Size for the Training Set

Not applicable. This device is a physical stent and delivery system, not an AI model requiring a training set.

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

Not applicable. As the device is not an AI model, there is no training set or ground truth in that context.

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