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Central Station's intended use is to retrospectively receive, display and store monitored vital signs parameters and related data. The Central Station displays the data and system alerts for review and interpretation by a healthcare professional. Central Station is not intended for emergency use or real-time monitoring.

Central Station is a software system that operates on a commercially available PC system with the minimum performance specifications consistent with typical PC hardware and equipment specifications. Central Station accepts data from Honeywell HomMed Patient Monitors (Sentry and Genesis) as well as the Honeywell HomMed MedPartner.

The provided text describes the Honeywell HomMed Central Station, Version 3.5, a software system for retrospectively receiving, displaying, and storing monitored vital signs parameters and related data.

Here's an analysis of the acceptance criteria and study information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state a specific "test set" in the traditional sense of a clinical or image-based study. The performance data section refers to "software validation results." This suggests that the testing was primarily focused on the software's functionality, data integrity, and compliance with specifications, rather than a clinical trial with patient data.

• Sample Size for Test Set: Not specified, but likely refers to various data inputs and scenarios used during software validation.
• Data Provenance: Not specified. Given the nature of a software validation for a "Central Station," it's probable that various types of simulated or recorded vital signs data would have been used to test the system's ability to receive, display, and store. It's not a direct patient study with geographical or temporal data provenance.

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

This information is not provided in the document. For a software system like the Central Station, "ground truth" would likely refer to the correctness of the data received, displayed, and stored, and the proper functioning of alerts based on predefined thresholds. The "experts" would likely be software testers, quality assurance personnel, and potentially clinical subject matter experts validating the accuracy of the displayed information against expected results. However, their numbers and specific qualifications are not mentioned.

4. Adjudication Method for the Test Set

This information is not provided in the document. Given that this is a software system for data display and storage, traditional adjudication methods for diagnostic images or clinical outcomes might not directly apply. The "adjudication" would likely be the verification of software outputs against expected behavior, potentially involving multiple testers or a structured review process, but no specific method is detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, an MRMC comparative effectiveness study was not done. The device is a "Central Station" software system, not an AI-powered diagnostic tool that directly assists human readers in interpreting medical images or making clinical diagnoses. Its function is to display and store vital signs data for review by healthcare professionals.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The entire study implicitly describes a standalone performance, as the "Central Station" software is the primary subject. The "software validation results demonstrated that the Central Station 3.5 System was in compliance... and that it performed within its specifications and functional requirements for software." This indicates a focus on the algorithm's direct functionality in processing and presenting data. However, it's crucial to understand that while the software operates standalone in processing, its intended use is for review and interpretation by a healthcare professional, meaning it's inherently part of a human-in-the-loop workflow.

7. Type of Ground Truth Used

The ground truth for this device would be defined by the correctness of software functionality and data integrity. This means:

• The system accurately receives data from specified patient monitors.
• The system accurately displays the vital signs parameters and related data.
• The system accurately stores the data.
• System alerts function correctly based on intended logic.
• The software complies with established guidelines and standards.

This is fundamentally a functional and performance validation against predefined software requirements and industry standards, rather than a clinical ground truth like pathology or patient outcomes.

8. Sample Size for the Training Set

This information is not applicable/not provided. The Honeywell HomMed Central Station is described as a software system that receives, displays, and stores vital signs data. It is not an AI/ML device that requires a "training set" in the context of machine learning model development. The software's logic and functionality are likely rule-based or algorithmic, programmed to perform specific tasks, rather than learned from a dataset.

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

This information is not applicable as there is no "training set" for this type of software device.

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