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The Philips SureSigns Central is intended for central viewing of physiologic waves, parameters and trends from other networked medical devices (patient monitors and vital signs monitors) for multiple patients. It provides secondary operator notification of alarms from other networked medical devices. It provides for the retrospective review of alarm conditions, physiologic waveforms and parameters from multiple patients. The intended use of the printer, when present, is to provide hardcopy text, graphics and waveform data .The Philips SureSigns Central may provide for connection and information exchange to external systems. The Philips SureSigns Central is intended for use in hospitals and out-of-hospital patient care settings (such as clinics, outpatient surgery facilities, long-term care facilities and physician offices) in which care is administered by healthcare professionals.

The subject device Philips SureSigns Central, model S863291, is a medical device software product that runs on a PC platform including a proprietary license key (ITE equipment).

The provided text describes the Philips SureSigns Central, a central station for viewing physiological data, but it does not contain the specific study details to directly populate all requested fields. The document is a 510(k) summary and FDA clearance letter, which focuses on demonstrating substantial equivalence to predicate devices rather than presenting a detailed clinical study report with specific acceptance criteria and performance metrics in the way your request outlines.

Here's an attempt to extract and infer information based on the provided text, with clear indications where information is not available from the document:

1. Table of Acceptance Criteria and Reported Device Performance

The document broadly states that "Pass/Fail criteria were based on the specifications cleared for the subject device and test results showed substantial equivalence." However, it does not provide a specific table of quantitative acceptance criteria or detailed performance metrics.

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

• Sample size for test set: Not specified. The document mentions "system level tests, performance tests, and safety testing from hazard analysis" but does not detail the number of cases, patients, or data points used in these tests.
• Data provenance (country of origin, retrospective/prospective): Not specified.

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

• Number of experts: Not applicable/not specified. This type of device (a central station) does not typically involve human expert adjudication for its core functionality in the way, for example, an AI diagnostic algorithm would. Its performance is validated against its specifications for data display, alarm handling, and communication, not against human expert diagnoses.
• Qualifications of experts: Not applicable/not specified.

4. Adjudication Method for the Test Set

• Adjudication method: Not applicable/not specified. As with point 3, the concept of expert adjudication for ground truth is not typically relevant for this type of device's validation.

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

• Was an MRMC study done? No. The document does not mention any MRMC study or comparison of human reader performance with or without AI assistance. This device is an information display and communication system, not an AI diagnostic tool intended to directly improve human reader performance through assistance.
• Effect size of human readers improvement with/without AI assistance: Not applicable, as no such study was conducted.

6. Standalone Performance Study

• Was a standalone (algorithm only) performance study done? A "standalone" performance study in the context of an algorithm's diagnostic accuracy is not explicitly detailed. However, the document does state that "Verification, validation, and testing activities establish the performance, functionality, and reliability characteristics of the subject device. Testing involved system level tests, performance tests, and safety testing from hazard analysis." This indicates that the device's functions (e.g., displaying waves, parameters, trends, alarm notification, data review) were tested to ensure they met their specifications independently. The "algorithm" here refers to the software's operational logic rather than a diagnostic AI algorithm.

7. Type of Ground Truth Used

• Type of ground truth: The "ground truth" for this device would be its own design specifications and the accuracy of the data input from compatible patient monitors, as well as the correct functioning of its display, alarm, and communication features. It's not based on expert consensus, pathology, or outcomes data in the traditional sense of a diagnostic device. The validation would verify that the device accurately reflects the data it receives and performs its intended functions according to its technical requirements.

8. Sample Size for the Training Set

• Sample size for training set: Not applicable/not specified. This device is a software product for data display and management, not an AI/machine learning model that undergoes "training" on a specific dataset. Its development involves software engineering testing rather than AI training.

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

• How ground truth was established: Not applicable, as there is no "training set" in the context of an AI model for this device.

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