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Extension Sets with the InterLink® Micro-Infusion Manifold are intended for use with a vascular access device for blood sampling and the administration of multiple drugs and solutions. These devices, like others containing the InterLink® injection site, are designed to reduce the risk of accidental needle sticks when used with the InterLink® cannula, as part of a "needleless" IV access system.

The proposed InterLink® Micro-Infusion Manifold will be combined with various configurations of extension sets for the administration of IV solutions. The manifold incorporates three InterLink® injection sites mounted to a central barrel or solution conduit. The manifold provides multiple InterLink® access sites for bolus injection or infusion of parenteral solutions into the intravenous line. The integrated design of the manifold facilitates administration of multiple infusions with minimal fluid residual volume. Use of the InterLink® Micro-Infusion Manifold and InterLink® cannula will reduce the risk of accidental needle sticks when accessing the IV line.

There is one material in the proposed manifold which is a material new to Baxter devices. A cyanoacrylate adhesive may be used to bond the checkvalve to the manifold housing and the manifold luer to the extension set tubing. The other materials in the proposed InterLink® Micro-Infusion Manifold and the extension sets incorporating it have been previously tested and used in other marketed devices for similar IV solution administration applications.

The provided text is a 510(k) summary for a medical device (InterLink® Micro-Infusion Manifold Extension Sets) seeking clearance from the FDA. This type of document focuses on demonstrating substantial equivalence to a predicate device, rather than providing a detailed study design and results for acceptance criteria in the manner of an AI/ML device study.

Therefore, many of the specific questions you've asked (e.g., sample size for test sets, number of experts for ground truth, MRMC study effect size, standalone performance, training set details) are not applicable to this type of submission. This document describes the equivalence of a physical medical device, not an AI/ML algorithm.

However, I can extract information related to the device's functional performance and general "acceptance criteria" as described in the document.

Acceptance Criteria and Reported Device Performance

Explanation Regarding Non-Applicable Sections:

• 1. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable. This refers to physical and biological testing of a hardware device, not data-driven AI/ML performance. The "test set" would be the manufactured devices themselves that undergo functional and biocompatibility testing. The document does not specify exact sample sizes for this internal validation.
• 2. 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. "Ground truth" in this context refers to the results of standard material and functional tests, not expert interpretation of medical images or data.
• 3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. This relates to expert consensus for AI/ML ground truth, not device testing.
• 4. 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 for AI/ML diagnostics, not a physical medical device.
• 5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This device is a physical component (extension set with manifold), not an algorithm.
• 6. The type of ground truth used (expert concensus, pathology, outcomes data, etc): For the material, the "ground truth" is established by adherence to recognized standards like ISO 10993-1 and USP, and the functional requirements are likely internal engineering specifications.
• 7. The sample size for the training set: Not applicable. There is no AI/ML "training set" for this device.
• 8. How the ground truth for the training set was established: Not applicable.

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