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The device is intended to monitor, display and record physiological data to provide cardiac and vital signs monitoring within a medical facility. The device is intended to monitor the electrocardiogram to generate audible and/or visible alarms when an arrhythmia exists. The device is also intended to monitor heart rate, pulse rate, blood oxygen saturation (SpO2), noninvasive blood pressure (NIBP), invasive blood pressure (IBP), body temperature, carbon dioxide concentration (CO2 and EtCO2), and respiratory rate. The device may generate an audible and/or visible alarm when a measured rate falls outside preset limits. The device may also condition and transmit physiological signals via radio frequency. This will be available for use by medical personnel on all patient populations.

The CGS-9001A Series Communication Gateway Server is an Optional Accessory to the BSM-2300A and BSM-6000 Bedside Monitors, it is a software product that allows the Nihon Kohden Patient Monitoring System to transfer alarm event information from the monitoring device to a third party system. The Communication Gateway Server collects alarm information from all networked patient monitors in a Nihon Kohden Patient Monitoring System and forwards it to a commercially available third party Secondary Alarm Notification System. The alarm notification generated by the third party alarm (not marketed by Nihon Kohden America, Inc.) is for Secondary Alarm Notification Only.

The provided text describes a 510(k) summary for the Nihon Kohden BSM-2300A and BSM-6000 Bedside Monitors with the CGS-9001A Communication Gateway Server. The document focuses on regulatory approval based on demonstrating substantial equivalence to a predicate device, rather than a clinical study establishing specific performance acceptance criteria for a new clinical indication.

Therefore, many of the requested elements for describing specific acceptance criteria and a study that proves the device meets them cannot be fully extracted from this document, as the submission primarily pertains to the safety and performance verification of a device accessory that facilitates alarm information transfer, not a diagnostic or therapeutic AI algorithm.

However, I can extract the available information and highlight what is not present.

1. Table of Acceptance Criteria and Reported Device Performance

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

This information is not explicitly provided. The documentation refers to "safety and performance testing procedures" but does not detail the sample size (e.g., number of patients, number of alarm events tested) for these tests, nor the data provenance (country of origin, retrospective/prospective). Since the device is a communication gateway for alarm events, the testing would likely involve system integration and alarm transmission validation, rather than a clinical dataset in the traditional sense.

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

This information is not provided. Given the nature of the device (a communication gateway accessory), the "ground truth" would likely involve technical validation of alarm transmission and accuracy, rather than clinical consensus by medical experts in diagnosing conditions.

4. Adjudication method for the test set

This information is not provided.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not done. This type of study is typically performed for diagnostic or AI-powered algorithms where human interpretation of medical images or data is involved. The CGS-9001A is an accessory to facilitate alarm notification and does not involve AI or direct human interpretation of complex medical data for diagnosis.

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

This is not applicable in the context of an "algorithm only" as found in AI/ML devices. The device's primary function is to collect and forward alarm information. Performance testing would likely focus on the accuracy and reliability of this transmission, which is a standalone function of the accessory. The document states "These tests verified that the device performed within specifications," confirming a form of standalone performance evaluation for its intended function.

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

The document does not explicitly state the type of "ground truth" used. For a communication gateway device, the ground truth would likely refer to the accurate transmission of alarm data as generated by the bedside monitors to a third-party system, verified against the actual alarm events occurring on the monitors. This would involve comparing the transmitted data with the source data rather than an external clinical ground truth like pathology or expert consensus.

8. The sample size for the training set

This information is not applicable and not provided. The CGS-9001A is a software product for alarm communication, not an AI/ML algorithm that requires a training set.

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

This information is not applicable and not provided, as there is no training set for this device.

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The device is intended to monitor, display and record physiological data to provide cardiac and vital signs monitoring within a medical facility. The device is intended to monitor the electrocardiogram to generate audible and/or visible alarms when an arrhythmia exists. The device is also intended to monitor heart rate, pulse rate, blood oxygen saturation (SpO2) noninvasive blood pressure (NIBP) invasive blood pressure (IBP), body temperature, carbon dioxide concentration (CO2 and EtCO2), and respiratory rate. The device may generate an audible and/or visual alarm when a measured rate falls outside preset limits. The device may also condition and transmit physiological signals via radio frequency. The device will be available for use by medical personnel on all patient populations.

The device is a multi-parameter monitor consisting of a color LCD touchscreen to display waveforms and numerics of monitored parameters, multi-parameter input unit (socket for input cables), and a removable battery pack. Options include a built-in thermal array recorder and network communication card. The device is software driven.

The provided document describes a 510(k) notification for the Nihon Kohden BSM-2300A Series Bedside Monitor. The primary focus of the performance testing section is on compliance with electrical safety, electromagnetic compatibility, and general functional specifications, rather than detailed clinical performance metrics for arrhythmia detection or specific vital sign monitoring accuracy.

Therefore, many of the requested elements regarding acceptance criteria for specific clinical performance, sample sizes for test sets with ground truth, expert adjudication, or MRMC studies are not available in the provided text. The document primarily focuses on demonstrating substantial equivalence to a predicate device through adherence to recognized electrical and safety standards and internal product specifications.

Here's a breakdown of the available information based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The provided text does not include specific numerical acceptance criteria for clinical performance metrics (e.g., sensitivity, specificity, accuracy for arrhythmia detection, or accuracy ranges for vital signs) nor corresponding reported numerical device performance for such criteria. The performance testing described is focused on engineering and safety standards compliance and internal product specifications.

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

• Sample Size for Test Set: Not specified for clinical performance. The document mentions "product verification and validation" for software and "completed testing" for environmental/safety, but no sample sizes for patients or data points are provided.
• Data Provenance: Not specified. Given the nature of the testing described (compliance with standards, software validation, environmental), it likely involved in-house engineering testing rather than patient data collected from a specific country. If patient data was used for validation of arrhythmia or vital signs, it is not mentioned.
• Retrospective or Prospective: Not specified.

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

Not applicable/Not specified. The document does not describe the establishment of ground truth for clinical performance using human experts. The testing described focuses on engineering and safety compliance.

4. Adjudication method for the test set

Not applicable/Not specified. There is no mention of clinical test sets requiring adjudication.

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

Not applicable. This device is a bedside monitor with automated arrhythmia and vital sign detection/alarms, not an AI-assisted diagnostic tool that would typically be evaluated with MRMC studies comparing human readers with and without AI assistance. The technology described predates widespread "AI" as we understand it in medical devices today.

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

Yes, implicitly. The device itself is designed to perform monitoring, detect arrhythmias, and generate alarms without continuous human intervention. The testing described (compliance with standards, software functionality) would inherently evaluate the device's standalone performance against its specifications and the requirements of the standards. However, specific standalone performance metrics (e.g., sensitivity/specificity for arrhythmia without human review) are not provided.

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

Not specified for clinical performance. For the engineering and safety tests, the "ground truth" would be the specifications and requirements defined by the applicable standards (e.g., IEC 60601-1, AAMI EC 12-27). For software, the ground truth would be the expected functional behavior defined in the product specifications.

8. The sample size for the training set

Not applicable/Not specified. This document is for a conventional medical device (bedside monitor) from 2001 and does not describe machine learning or AI models requiring training sets as typically understood in current contexts.

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

Not applicable/Not specified, as no training set for a machine learning model is mentioned.

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