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The indications for use include general (including urologic, thoracic, plastic and reconstructive, arthroscopic), laparoscopic, and gynecologic procedures where electrosurgical cutting and coagulation of tissue, and sealing (fusion) of vessels and tissue bundles is performed, including such procedures as bowel resections, hysterectomies (both vaginal and abdominal). laparoscopic cholesystectomies, laparoscopically assisted vaqinal hysterectomies, gall bladder procedures, Nissen fundoplication, adhesilysis, oophorectomy, etc. Vessels (arteries, veins, pulmonary arteries, pulmonary veins, lymph) 7mm and smaller in diameter, and bundles as large as will fit in the jaws of the devices can be sealed with the LigaSure™ vessel sealing (tissue fusion) output.

The ForceTriad™ generator is a full-featured electrosurgical generator with monopolar, bipolar, and LigaSure™ vessel sealing outputs. The generator is an electrically isolated, microcontroller-based device, incorporating closed-loop control for all output modes implemented in the microcontroller firmware. The generator incorporates Instant Response™ technology to constantly measure the electrical impedance of the tissue and instantaneously adjust the generator output to maintain the desired power.

Here's an analysis of the provided text regarding the acceptance criteria and study for the ForceTriad™ Electrosurgical Generator:

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample Size Used for the Test Set and Data Provenance:

The document explicitly states: "No clinical testing was conducted." Therefore, there is no test set in the traditional sense of patient data. The evaluation was based on non-clinical testing, including software validation and bench testing. The provenance of this non-clinical test data is not specified in terms of country of origin, but it is implied to be internal testing by the manufacturer (Covidien). The testing itself would be considered prospective for the specific software change being evaluated.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications:

As no clinical testing was performed and the evaluation focused on software and bench testing, there were no "experts" in the sense of clinical professionals establishing a ground truth on patient data. The ground truth for the non-clinical tests would have been established by engineering and quality assurance personnel, likely using predefined specifications and expected outputs for the software and hardware interfaces. Their specific qualifications are not detailed in this summary.

4. Adjudication Method for the Test Set:

Not applicable, as no clinical test set with human assessment was utilized. The "ground truth" for the non-clinical tests would have been determined through objective measurements and comparisons against technical specifications, not through a consensus-based adjudication process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The document explicitly states, "No clinical testing was conducted." Therefore, there is no information about the effect size of human readers with vs. without AI assistance. This submission focuses on a software change for integration, not on a new diagnostic or interpretative AI feature.

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

This device is an electrosurgical generator, which is inherently a human-in-the-loop device. However, the specific software change being evaluated (allowing the generator to interface with an Integrated OR system) was likely tested in a standalone manner relative to its function of communication. The bench testing and software validation would assess the algorithm's ability to correctly interface and transmit data without direct human intervention once configured, but the overall device always requires human operation. The document doesn't explicitly describe a "standalone performance" study in the context of an AI algorithm making decisions, as this is not an AI-driven diagnostic device.

7. The Type of Ground Truth Used:

The ground truth used for this submission was primarily technical specifications and expected system behavior. For software validation, this would involve comparing actual software outputs against designed functional requirements. For bench testing, it would involve verifying that the device communicated correctly with the simulated and actual Olympus EndoAlpha system according to predefined communication protocols and interface standards.

8. The Sample Size for the Training Set:

Not applicable. This submission is for a software modification to an existing device for interoperability. There is no mention of a machine learning or artificial intelligence component that would require a "training set" in the traditional sense. The software development process would involve iterative testing and debugging, but not a distinct training set for an AI model.

9. How the Ground Truth for the Training Set Was Established:

Not applicable, as there was no training set in the context of machine learning. The "ground truth" for the software development and validation would have been based on the functional specifications and design documents for the integration with the Olympus EndoAlpha endosurgery system, established by Covidien's engineering team.

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