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VVR Generator system is intended for endovascular coagulation of Saphenous Veins in patients with superficial vein reflux.

VVR Generator System consists of Generator (VVR Generator), Active electrode (VENISTAR) and accessory such as foot switch and cooling pump.
The generator generates, controls and monitors RF energy and delivers energy to the active electrode.
The active electrode has an active coil and a receiver coil that allow the RF energy to flow through the tissue. The RF energy raises the temperature in the tissue, causing coagulation and occlusion of blood vessels.
The single footswitch works as an on/off switch and the double footswitch works to increase the power output.
A cooling pump circulates water around the electrodes to reduce the temperature of the electrodes and prevent tissue sticking to the electrodes.

The provided text does not contain information about acceptance criteria and a study proving a device meets acceptance criteria in the usual sense of a diagnostic or AI device that relies on performance metrics like sensitivity, specificity, or accuracy.

Instead, the document is a 510(k) Premarket Notification for a medical device called the "VVR Generator System," which is an electrosurgical cutting and coagulation device. The purpose of this notification is to demonstrate that the new device is "substantially equivalent" to legally marketed predicate devices.

The "study" described is a compilation of non-clinical data, animal studies, and referenced clinical studies to support this claim of substantial equivalence, not to meet specific performance acceptance criteria for a diagnostic output.

Therefore, many of the requested categories in your prompt will not be directly applicable, as they relate to the performance evaluation of a diagnostic or AI algorithm, which is not the nature of this submission.

However, I can extract information related to the closest concepts in the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't define "acceptance criteria" in terms of specific performance metrics (like accuracy, sensitivity, specificity) for an AI or diagnostic device output. Instead, the "acceptance" is demonstrated through substantial equivalence to predicate devices. The "reported device performance" is framed in terms of similar technical characteristics, thermal effects, and clinical outcomes compared to the predicate devices.

2. Sample size used for the test set and the data provenance:

• Animal Study:
  • Sample Size: 7 dogs
  • Origin: Animal model (dog)
• Clinical Study (referenced):
  • Sample Size: 60 patients (41 males, 19 females)
  • Origin: Not explicitly stated, but implies real-world data (RWD) from a clinical setting where the device was used. This was a referenced study, not one conducted directly for this 510(k) submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

This information is not provided in the document. For the animal study, evaluation methods (macroscopic, microscopic, ultrasonographic) are mentioned, but not the number or qualifications of experts. For the clinical study, it's a referenced study reporting outcomes.

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

This information is not provided in the document.

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:

This device is an electrosurgical system, not an AI or diagnostic imaging device. Therefore, an MRMC study comparing human reader performance with or without AI assistance is not applicable and was not performed/reported.

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

This device is an electrosurgical device, not an algorithm or AI. Performance is assessed through its direct physical and physiological effects, not through an algorithm's standalone diagnostic capabilities. So, this question is not applicable.

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

• Animal Study:
  • Macroscopic: 2,3,5-triphenyltetrazolium chloride stain (TTC) evaluation for tissue viability/injury.
  • Microscopic: Hematoxylin and eosin (H&E) stain for histological evaluation.
  • Ultrasonographic: For assessment of blood flow and vein status.
• Clinical Study:
  • Outcomes Data: Reported "success rate" and "complications" which are clinical outcomes.

8. The sample size for the training set:

This device is an electrosurgical system, not an AI or machine learning model that requires a training set. So, this question is not applicable.

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

As this is not an AI/ML device, there is no "training set" or ground truth established for one. So, this question is not applicable.

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