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The Subject device of this premarket notification submission is indicated for use to cut and or coagulate during laparoscopic surgical procedures.

The Aaron SlickTip Solid Laparoscopic Electrode is a sterile, single use solid laparoscopic electrode. It utilizes an insulated stainless steel laparoscopic electrode with the same coating as cleared under K974735. It is an accessory to an electrosurgical generator.

This document is a 510(k) premarket notification for the Aaron SlickTip Solid Laparoscopic Electrode. It declares substantial equivalence to previously cleared devices rather than presenting novel performance data based on a new study. Therefore, the information requested about acceptance criteria, study design, and performance metrics is generally not applicable in the context of this specific regulatory submission.

However, I can extract information related to the device comparison and the regulatory determination.

Here's an analysis based on the provided text:

1. Table of acceptance criteria and the reported device performance:

This section is Not Applicable in the traditional sense for a 510(k) submission asserting substantial equivalence. 510(k)s often don't establish new, quantifiable performance criteria for the device itself but rather demonstrate that the new device is as safe and effective as a predicate device. The "acceptance criteria" here implicitly refer to demonstrating equivalence in design, operation, intended use, materials, components, and performance claims to the listed predicate devices.

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

• Sample size: Not explicitly stated as a separate "test set" with a number of units. The testing appears to be on the device itself for hazard analysis and to confirm its performance characteristics relative to predicates, rather than on a patient population.
• Data provenance: Not specified. It's implied that testing was conducted internally by Aaron Medical Industries, Inc., as part of their product development and regulatory submission.

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

This is Not Applicable. The determination of "ground truth" through expert consensus or clinical outcomes is typical for diagnostic algorithms or subjective assessments. For a medical device like a laparoscopic electrode, the ground truth for its function is typically based on engineering specifications, material properties, and performance against established benchmarks or predicate devices. The "ground truth" for safety and efficacy in this context is established by comparison to legally marketed predicate devices, as assessed by the FDA.

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

This is Not Applicable. Adjudication methods are relevant for studies involving human interpretation (e.g., radiologists reviewing images). For the hazard analysis and substantial equivalence claim of this device, a formal adjudication method as described is not relevant.

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 is Not Applicable. This device is a surgical tool, not an AI or diagnostic imaging device. Therefore, MRMC studies are not relevant to its regulatory review.

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

This is Not Applicable. This device is a physical medical instrument, not an algorithm.

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

The "ground truth" in this context is based on the established safety and effectiveness of the predicate devices. The new device is considered safe and effective if it performs with the same intended use and performance characteristics as the predicate devices and introduces no new safety concerns. This is demonstrated through engineering comparison, materials assessment, and hazard analysis, rather than clinical outcome data for a novel device.

8. The sample size for the training set:

This is Not Applicable. This is a physical device, not an AI algorithm. There is no concept of a "training set" in this submission.

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

This is Not Applicable for the same reason as point 8.

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