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The XenX device is intended to be used endoscopically to bypass and entrap calculi from the urinary tract, to prevent retrograde migration of calculi during laser lithotripsy and to facilitate the placement of endourological instruments during diagnostic or interventional procedures.

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I am sorry, but the provided text does not contain the information required to answer your request. The document describes an FDA 510(k) clearance for the XenX™ Stone Management Device and outlines its indications for use, but it does not detail any acceptance criteria, study data, sample sizes, expert qualifications, or ground truth establishment methods for a device performance study. This document is a regulatory clearance letter, not a study report.

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The XenX device is intended to be used endoscopically to bypass and entrap calculi from the urinary tract, to prevent retrograde migration of calculi during laser lithotripsy,and to facilitate the placement of endourological instruments during diagnostic or interventional procedures

The XenX device combines several features in one device which facilitate an endoscopic procedure. The XenX easily tracks past ureteral kidney stone and is navigated under fluoroscopy like a urologic guidewire. Once in place, a self expandable braided structure is deployed to block the ureteral lumen and prevent stone particle migration towards the kidney. The braided structure enables a constant irrigation flow for clear uretroscopic vision during the procedure. Following stone fragmentation and if required the physician can choose to propagate a urinary stent over the device for ureteral placement.

Here's an analysis of the provided text regarding the XenX device, structured according to your request.

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly list quantitative acceptance criteria with specific thresholds for device performance. Instead, it states that "Testing showed that the XenX is as safe, effective and performs as well as, or better than the predicates." The performance data section describes a series of nonclinical tests conducted to demonstrate equivalence to predicate devices.

2. Sample Size for Test Set and Data Provenance

The 510(k) summary does not provide information on the sample size used for any test set or the data provenance (e.g., country of origin, retrospective/prospective). The studies mentioned are "nonclinical testing," implying bench testing or in-vitro studies, not human clinical trials with patient data.

3. Number of Experts and Qualifications for Ground Truth

This information is not provided as the studies are "nonclinical testing" and do not involve human expert assessment of a test set for ground truth establishment.

4. Adjudication Method

This information is not applicable/not provided as there is no mention of a clinical test set requiring expert adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

A multi-reader multi-case (MRMC) comparative effectiveness study was not conducted (or at least not reported in this summary). The studies are nonclinical and do not involve human readers or AI assistance.

6. Standalone (Algorithm Only) Performance Study

This is not applicable as the XenX is a physical medical device (guidewire with a braided structure), not an AI algorithm. Hence, standalone performance for an algorithm is not relevant.

7. Type of Ground Truth Used

The ground truth for the nonclinical tests would have been established by physical measurements, engineering specifications, and established testing protocols for medical device performance. For example, a "Tensile strength" test would have a defined breaking point or force resistance that the device must meet, which serves as the "ground truth" for that particular performance metric. There is no mention of expert consensus, pathology, or outcomes data, as these are typically associated with clinical studies.

8. Sample Size for Training Set

There is no mention of a training set sample size, as this device does not involve machine learning or AI that would require a "training set."

9. How Ground Truth for Training Set Was Established

This is not applicable for the same reason as point 8.

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