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The DASH 3000/4000/5000 Patient Monitor is intended for use under the direct supervision of a licensed healthcare practitioner. The intended use of the system is to monitor physiologic parameter data on adult, pediatric and neonatal patients. The DASH 3000/4000/5000 is designed as a bedside, portable, and transport monitor that can operate in all professional medical facilities and medical transport modes including but not limited to: emergency department, operating room, post anesthesia recovery, critical care, surgical intensive care, respiratory intensive care, coronary care, medical intensive care, pediatric intensive care, or neonatal intensive care areas located in hospitals, outpatient clinics, freestanding surgical centers, and other alternate care facilities, intra-hospital patient transport, inter-hospital patient transport via ground vehicles (i.e., ambulance, etc.) and fixed and rotary winged aircraft, and pre-hospital emergency response.

Physiologic data includes but is not restricted to: electrocardiogram, invasive blood pressure, noninvasive blood pressure, pulse, temperature, cardiac output, respiration, pulse oximetry, carbon dioxide, oxygen, and anesthetic agents as summarized in the operator's manual.

The DASH 3000/4000/5000 Patient Monitor is also intended to provide physiologic data over the Unity network to clinical information systems and allow the user to access hospital data at the point-of-care.

This information can be displayed, trended, stored, and printed.

The DASH 3000/4000/5000 Patient Monitor was developed to interface with nonproprietary third party peripheral devices that support serial data outputs.

The DASH 3000/4000/5000 Patient Monitor is a device that is designed to be used to monitor, display, and print a patient's basic physiological parameters including: electrocardiography (ECG), invasive blood pressure, non-invasive blood pressure, oxygen saturation, temperature, impedance respiration, end-tidal carbon dioxide, oxygen, nitrous oxide and anesthetic agents. Other features include arrhythmia, cardiac output, cardiac and pulmonary calculations, dose calculations, PA wedge, ST analysis, and interpretive 12 lead ECG analysis (12SL). Additionally, the network interface allows for the display and transfer of network available patient data.

The provided text is a 510(k) summary for the GE Medical Systems Information Technologies DASH 3000/4000/5000 Patient Monitor. It focuses on demonstrating substantial equivalence to predicate devices and detailing the monitor's intended use and functionality.

However, the summary does not contain the specific information requested regarding acceptance criteria and a study proving the device meets those criteria, as typically found in detailed performance studies. It primarily states that the device "complies with the voluntary standards as detailed in Section 9 of this submission" (which is not provided in the extract) and that "The results of these measurements demonstrated that the DASH 3000/4000/5000 Patient Monitor is as safe, as effective, and performs as well as the predicate devices."

Therefore, I cannot populate the requested table and answer the study-specific questions based solely on the provided text. The submission itself likely contained the detailed test results and acceptance criteria, but they are not included in this summary.

Based on the provided text, here’s what can be inferred and what is missing:

1. Table of acceptance criteria and the reported device performance:

This information is not provided in the document. The document states "The DASH 3000/4000/5000 Patient Monitor complies with the voluntary standards as detailed in Section 9 of this submission." Section 9, which would contain the details of these standards, specific acceptance criteria, and specific performance results, is not included.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

This information is not provided. The document mentions general "Software and hardware testing," "Safety testing," and "Environmental testing" but does not detail the methodology, sample sizes, or data provenance of these tests as they pertain to specific performance metrics.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

This information is not provided. The device is a physiological patient monitor, and its performance would typically be evaluated against established physiological measurement standards, not by expert interpretation in the way an imaging device might be.

4. Adjudication method (e.g. 2+1, 3+1, none) 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:

This information is not provided. MRMC studies are typically for diagnostic imaging devices where human interpretation is a key component, and AI assistance can augment that. This device is a physiological monitor, not an AI-assisted diagnostic tool in the sense of image analysis.

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

This information is not provided explicitly, but as a physiological monitor, its primary function is standalone measurement and display of parameters. The tests mentioned (software, hardware, safety, environmental) suggest standalone performance was evaluated, but details are lacking.

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

This information is not explicitly stated, but for physiological monitors, the ground truth would typically be established by comparison to highly accurate reference instruments or established clinical methodologies for each physiological parameter (e.g., a known pressure source for BP accuracy, a calibrated gas analyzer for CO2 measurement, ECG simulation for arrhythmia detection).

8. The sample size for the training set:

This information is not provided. The document describes the system development process ("Requirements specification review," "Code inspections," "Software and hardware testing," etc.) but doesn't mention a "training set" in the context of an AI/machine learning model. This indicates the device likely uses established algorithms and signal processing techniques rather than a machine learning approach that requires a separate training set for model development.

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

This information is not provided, as a "training set" in the context of machine learning is not mentioned or implied for this device's development as described.

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