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Core.nova (core.browser, core.connect with driver, IR Transceiver, core.media, and core.portal) are components used for the central control of integrated medical devices and accessories in one central location within the operating room. In particular core.nova is used for the control of connected compatible OR equipment, like OR-lamp, OR-camera, ORtable, insufflator, or video- and documentation system intended for diagnostic and therapeutic medical conditions. The connected devices (Richard Wolf or third party) can be control terminals (with touch screen), or the control mechanism on actual device.

The core nova (complete operating room endoscopy nova) by Richard Wolf is a networkbased integrated operating room accessory that provides central control to various Richard Wolf devices and third party devices. This control allows the user to control device settings from the sterile area. Richard Wolf's core nova is comprised of core.browser, core.connect with driver, IR Transceiver, an optional core.media, and an optional server core.portal.

The provided document is a 510(k) premarket notification for the "core nova Complete Operating Room Endoscopy" system by Richard Wolf. This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study with specific acceptance criteria and detailed device performance metrics in the way one might expect for a diagnostic or therapeutic algorithm.

Therefore, the requested information, particularly regarding acceptance criteria, sample sizes, expert ground truth, adjudication methods, MRMC studies, and standalone performance related to diagnostic or therapeutic effectiveness, is not present in this document. The device in question, an endoscopic central control unit, is a system for managing and controlling other medical devices in an operating room, rather than a device that directly yields a diagnostic output or performs a direct therapeutic action that would typically require such performance studies.

However, I can extract information related to the device's validation based on the document's content:

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the document doesn't provide acceptance criteria and performance in the typical sense for a diagnostic device (e.g., sensitivity, specificity). The "performance" described here relates to compliance with regulatory standards for medical electrical equipment and software.

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

• Sample size: Not applicable/Not provided. The validation described is compliance with electrical and software standards, not performance on a dataset of patient data.
• Data provenance: Not applicable.

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

• Not applicable. The "ground truth" here is compliance with established engineering and software safety standards, assessed through testing, not expert interpretation of medical data.

4. Adjudication method for the test set:

• Not applicable.

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 MRMC study was done. This device is not an AI-assisted diagnostic tool.

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

• Not applicable. This device is an operating room control system, not a standalone diagnostic or therapeutic algorithm.

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

• The "ground truth" in this context is the adherence of the device to established international and national electrical safety and electromagnetic compatibility (EMC) standards and compliance with FDA software validation guidelines. This is verified through engineering tests and software verification documentation, not medical ground truth like pathology.

8. The sample size for the training set:

• Not applicable. This device is not a machine learning or AI algorithm that requires a training set in the conventional sense.

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

• Not applicable.

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