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The Alaris® PC Unit is the main user interface unit and power supply of the Alaris® System, a modular system to be used with Alaris® System modules (aka Medley™ System modules) intended for use in today's growing professional healthcare environment for facilities that utilize infusion and/or monitoring devices. The specific intended use for each Alaris® System module is specified in its respective submission.

Device Description

The Alaris System is a modular system that consists of a point-of-care unit (PC unit) that provides the main user interface and power supply for the associated infusion and monitoring modules. This update is only for the Alaris PC unit Module 8000 software correction and does not require any change to the associated modules, systems, or accessories of the Alaris System. The basic functionality and existing features as described in the original and subsequent 510(k) submissions for the Alaris System and associated modules will not change.

AI/ML Overview

The provided text is a 510(k) summary for a software correction to an existing infusion pump, the Alaris® PC unit Module 8000. It doesn't contain information about the device's acceptance criteria or a study proving that it meets specific performance metrics. It primarily focuses on demonstrating substantial equivalence to a predicate device and outlining the administrative details of the submission.

Therefore, many of the requested categories related to performance studies and ground truth cannot be extracted from this document.

Here's a breakdown of what can and cannot be answered based on the provided input:

Section	Information from provided text
1. A table of acceptance criteria and the reported device performance	Not available. The document states: "The basic functionality and existing features as described in the original and subsequent 510(k) submissions for the Alaris System and associated modules will not change." This implies that the software correction did not alter the core performance characteristics that would typically have acceptance criteria. The submission is for a software correction, not a new device with new performance claims. Therefore, specific acceptance criteria and reported device performance metrics are not detailed in this 510(k) summary.
2. Sample size used for the test set and the data provenance	Not available. A test set or data provenance for performance evaluation is not mentioned. The submission is for a software correction to an existing device, and the focus is on maintaining equivalence rather than demonstrating new performance through a clinical or technical study with a specific test set.
3. Number of experts used to establish the ground truth for the test set and their qualifications	Not applicable/Not available. As there's no mention of a test set or ground truth establishment in the context of device performance in this document, expert involvement for this purpose is not discussed.
4. Adjudication method for the test set	Not applicable/Not available. No test set or related adjudication method is described.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size	Not applicable. This submission is for a software correction to an infusion pump, which is a medical device that performs a specific function (infusion of fluids/medications) and not an imaging or diagnostic device typically evaluated with MRMC studies or human reader performance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done	Not applicable. The device is an infusion pump with a software correction. Its performance is inherent to the machine's operation, not an "algorithm only" in the sense of AI for image analysis or diagnosis. The entire device, including its software, acts as a standalone system for its intended function (infusion). However, a specific "standalone performance study" in the context of AI evaluation is not applicable here as it's not an AI-driven diagnostic tool.
7. The type of ground truth used	Not applicable/Not available. The document does not describe performance metrics that would require external ground truth (e.g., pathology, outcomes data) to validate a diagnostic or predictive algorithm. The software correction's validity would likely be assessed through internal software validation, verification testing, and risk analysis, demonstrating that the correction resolves the identified issue without introducing new problems or altering the device's fundamental safe and effective operation.
8. The sample size for the training set	Not applicable/Not available. The software correction described is for an existing infusion pump. This is not an AI/ML device that typically involves a "training set" for model development. The software development process would involve various testing phases (e.g., unit testing, integration testing, system testing, regression testing) but not a "training set" in the context of machine learning.
9. How the ground truth for the training set was established	Not applicable/Not available. As there is no training set mentioned, there is no information on how its ground truth would be established. The "ground truth" for a software correction would relate to the correctness of the code's behavior against its specifications and requirements, as verified through standard software testing methodologies. This is different from the ground truth established for AI/ML performance evaluation (e.g., expert labels for images).

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