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The ClearMedical Reprocessed Endopath® Tristar® Blunt Tip Surgical Trocar, ClearMedical Reprocessed Endopath® Tristar® Surgical Trocar, and ClearMedical Reprocessed Endopath® Dilating Tip Trocar have application in thoracic, gynecologic laparoscopy, and other abdominal procedures to establish a path of entry for minimally invasive instruments.

Reprocessed endoscopic trocars consist of an obturator and sleeve. The trocar sleeve has a stopcock valve for gas insufflation and a desufflation lever for gas evacuation. The trocar obturator are either flat bladed, pyramidal, or blunt tip. Bladed and dilating tip obturators have a safety shield that exposes the blade during insertion and retract over the tip after the operative cavity has been penetrated.

The provided text is a 510(k) summary for ClearMedical Reprocessed Trocars. It focuses on demonstrating substantial equivalence to predicate devices for regulatory clearance, rather than reporting on a clinical or performance study of an AI/algorithm-driven device. Therefore, much of the requested information regarding acceptance criteria for an AI device, sample sizes for training/test sets, expert ground truth, MRMC studies, or standalone algorithm performance is not applicable or cannot be extracted from this document.

However, I can extract the general acceptance criteria and the type of information provided to "prove" the device meets them in the context of this 510(k) submission.

Acceptance Criteria and Device Performance (based on the provided 510(k) summary):

Study Information (Based on the provided document):

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

   • Not applicable / Not specified. This document describes a 510(k) submission for reprocessed medical devices, not an AI or algorithm-driven device that would typically involve a "test set" in the computational sense. The "test data" mentioned refers to validation of cleaning, performance, packaging, sterilization, and biocompatibility, which are laboratory/engineering tests on the reprocessed devices themselves. The specific sample sizes for these validation tests are not detailed in this summary.

2. 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 / Not specified. Ground truth, in the context of AI/algorithm performance, is not relevant to this submission. The "truth" for reprocessed devices is assessed through engineering and biological safety tests.

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

   • Not applicable / Not specified.

4. 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, not applicable. This is not an AI-assisted device.

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

   • No, not applicable. This is not an algorithm.

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

   • Not applicable in the typical AI sense. For this device, the "ground truth" equivalent would be the objective standards and specifications for the original predicate devices, and the established regulatory requirements for reprocessing, cleaning, sterilization, and biocompatibility. The performance (e.g., integrity, functionality) of the reprocessed devices is compared against these engineering and safety benchmarks.

7. The sample size for the training set:

   • Not applicable / Not specified. This is not an AI/machine learning device.

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

   • Not applicable / Not specified.

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