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The subject device is intended to facilitate subcutaneous infusion of fluids.

The Deltec Subcutaneous Infusion Set consists of a needle with attached wing, extension tubing and female luer.

This document is a 510(k) summary for a medical device called the "Deltec Subcutaneous Infusion Set." It seems to be for a simple, non-AI-powered device. Therefore, the questions related to AI/algorithm performance, ground truth, expert adjudication, and training/test sets are not applicable.

Here's how to address the relevant parts of your request based on the provided text:

1. A table of acceptance criteria and the reported device performance

Note: The document states the device is "designed to meet certain functional requirements related to tubing to needle and luer to tubing bond strength" but does not explicitly provide numerical acceptance criteria or specific test results. The conclusion that "the Deltec Subcutaneous Infusion Set is suitable for its intended use" implies these functional requirements were met.

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

Not applicable. This device is a physical infusion set, not an AI/algorithm-driven device that uses a "test set" in the context of data. No clinical studies were performed.

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)

Not applicable. This is not an AI/algorithm device requiring ground truth established by experts.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable.

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 is not an AI/algorithm device.

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

Not applicable. This is a physical medical device, not an algorithm.

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

Not applicable.

8. The sample size for the training set

Not applicable.

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

Not applicable.

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