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The Shinmed Various Models of Electro-Surgical Pencils, SW12200, SW12202, SW12300 used with a 510K-clearance generator is intended for use for ablation, removal, resection and coagulation of soft tissue and where associated hemostasis is required in open, endoscopic and laparoscopic surgical procedures. The device is intended for use by qualified medical personnel trained in the use of electrosurgical equipment.

The Shinmed Various Model: of reusable Electro-Surgical Pencils, SW11100, SW11200, SW11202, SW11300 used with a 510K-clearance generator is intended for use for ablation, removal, resection and coagulation of soft tissue and where associated hemostasis is required in open, endoscopic and laparoscopic surgical procedures. The cevice is intended for use by qualified medical personnel trained in the use of electrosurgical equipment.

The SHINMED Various Models of Electro-Surgical Pencils used with a 510k-clearance generator are intended for use for ablation, removal, resection and coagulation of soft tissue and where associated hemostasis is required in open, endoscopic and laparoscopic surgical procedures. The device is intended for use by qualified medical personnel trained in the use of electrosurgical equipment.

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The provided text is a 510(k) summary for SHINMED electrosurgical pencils. It primarily focuses on demonstrating substantial equivalence to a predicate device and outlines the intended use. However, it does not contain specific acceptance criteria, details of a performance study, or reported device performance metrics like sensitivity, specificity, or AUC, as would be expected for an AI/ML-driven medical device.

Therefore, many of the requested sections regarding acceptance criteria, study details, and performance metrics cannot be directly extracted from this document because it describes a traditional electrosurgical device, not an AI/ML system.

Here's a breakdown of what can and cannot be answered based on the provided text:

1. A table of acceptance criteria and the reported device performance

• Cannot be provided. The document is for an electrosurgical pencil, not an AI/ML device, and thus does not specify algorithmic performance metrics like sensitivity, specificity, or AUC against acceptance criteria. The "performance testing" mentioned is general validation for a medical device's function, not a specific study with AI-relevant metrics.

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

• Cannot be provided. This information is relevant for AI/ML device studies. The provided text doesn't describe a study with a "test set" in this context. It mentions "performance testing" to validate the device's function, but no details on sample size or data provenance in an AI/ML sense are given.

3. 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)

• Cannot be provided. This information pertains to AI/ML model validation where ground truth is established for test data. The document describes an electrosurgical pencil, which doesn't involve such expert-driven ground truth establishment for its performance validation.

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

• Cannot be provided. This is relevant for AI/ML model validation, particularly when experts interpret data to establish ground truth. This concept does not apply to the electrosurgical device described.

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

• No, an MRMC comparative effectiveness study was not done. This type of study is specific to AI/ML systems where human readers interact with AI outputs. The document is for an electrosurgical pencil, not an AI-assisted diagnostic or therapeutic device.

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

• No, a standalone algorithm performance study was not done. This applies to AI/ML algorithms. The device is a physical electrosurgical pencil.

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

• Cannot be provided. This is a concept related to evaluating AI/ML models against a reference standard. For an electrosurgical pencil, "ground truth" would relate to its physical and electrical performance in a lab setting, not a medical "ground truth" from patient data.

8. The sample size for the training set

• Cannot be provided. This information is pertinent to AI/ML model development. There is no concept of a "training set" for the electrosurgical pencils described.

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

• Cannot be provided. Similar to the above, this is relevant for AI/ML model training. This document does not describe an AI/ML system.

Summary based on available information:

The provided 510(k) summary is for SHINMED various models of electrosurgical pencils (disposable models SW12200, SW12202, SW12300, and reusable models SW11100, SW11200, SW11202, SW11300).

The document states that the devices were compared to a legally marketed predicate device, the Gyrus PlasmaKinetic Superpulse System (510K No - K031085), with respect to intended use and technological characteristics.

Study Type:
The document mentions "performance testing has been done to validate the performance of the device." This implies traditional engineering and functional testing for a medical device to ensure it meets its intended specifications for cutting and coagulation, and hemostasis. However, no specific details about the methodology, sample sizes, or quantitative results of these performance tests are provided in this 510(k) summary. The focus of the 510(k) is to demonstrate substantial equivalence to a predicate device.

Conclusion:
This document details the submission for an electrosurgical pencil, a hardware medical device, and not an AI/ML-driven device. Therefore, the specific information requested, which is tailored for AI/ML performance evaluation (like acceptance criteria for AI metrics, test/training set details, expert ground truth establishment, MRMC studies, or standalone algorithm performance), is not present in the provided text. The "study" referenced is general performance validation for a hardware device against its functional requirements, rather than a clinical study evaluating diagnostic or prognostic accuracy of an algorithm.

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