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The Unity Network ID is indicated for use in data collection and clinical information management through networks with independent bedside devices. The Unity Network ID is not intended for monitoring purposes, nor is the Unity Network ID intended to control any of the ctinical devices (independent bedside devices/ information systems) it is connected to.

The Unity Network ID system communicates patient data from sources other than GE Medical Systems Information Technologies, Inc. equipment to a clinical information system, central station, and/or GE Medical Systems Information Technologies Inc. patient monitors. The Unity Network ID acquires digital data from eight serial ports, converts the data to Unity Network protocols, and transmits the data over the monitoring network to a Unity Network device such as a patient monitor, clinical information system or central station.

The provided text describes the Unity Network ID, a physiological patient monitor, and its 510(k) submission. However, it does not contain the specific details required to answer all parts of your request, particularly regarding acceptance criteria, performance data, sample sizes, expert ground truth establishment, or multi-reader multi-case studies.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (K071982 Unity Network ID) through a design modification. The "Test Summary" section lists general quality assurance measures applied during development, but not specific acceptance criteria or quantitative performance results.

Here's what can be extracted and what is missing:

Acceptance Criteria and Reported Device Performance

• Risk Analysis
• Requirements Reviews
• Design Reviews
• Subsystem Verification
• Integration testing (System verification)
• Final acceptance testing (Validation)
• Performance testing
• Safety testing
• Environmental testing |

Study Details

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

   • Sample Size: Not specified. The document mentions "measurements" and "testing" but does not provide any information on the size of the test set (e.g., number of patients, data points, or test cases).
   • Data Provenance: Not specified. There is no information regarding the country of origin of the data or whether it was retrospective or prospective.

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

   • Not specified. The document does not describe the establishment of a "ground truth" using human experts for the test set. The evaluation seems to be based on engineering and validation testing against internal standards and the predicate device's functionality.

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

   • Not applicable/Not specified. Since there's no mention of expert-based ground truth or performance assessment involving human interpretation, an adjudication method is not described.

4. 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. The document makes no mention of an MRMC study. The device is a "Unity Network ID" system that communicates patient data; it is not an AI-assisted diagnostic tool for human readers.

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

   • The device itself appears to be a "standalone" system in the sense that it processes data without direct human-in-the-loop diagnostic interpretation. The document describes it as acquiring, converting, and transmitting data. The "performance testing" and "final acceptance testing" would assess its functional performance in this standalone capacity, but no detailed results for this are provided.

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

   • Not explicitly defined in the context of expert-based ground truth. The "ground truth" for this device's validation appears to be its ability to correctly acquire, convert, and transmit digital data according to specifications and in a manner "as safe, as effective, and performs as well as the predicate device." This would typically be assessed through engineering verification and validation against functional requirements and possibly relevant industry standards, rather than clinical outcomes or expert consensus on medical findings.

7. The sample size for the training set:

   • Not applicable/Not specified. The Unity Network ID primarily acts as a data communication and management system. It's not described as an AI/ML device that requires a "training set" in the conventional sense for learning patterns or making diagnostic predictions. Its development involves "Risk Analysis," "Requirements Reviews," "Design Reviews," etc., which are typical for traditional software and hardware development, not machine learning model training.

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

   • Not applicable/Not specified, as there is no mention of a training set for an AI/ML model.

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