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Plastimed's Seldiflex® central venous catheter is intended for long-term vascular access and is indicated for use with patients who require central venous pressure monitoring, I.V. fluids, blood products, drugs, or parenteral nutrition solutions.

The Seldiflex® is a single lumen, radiopaque polyurethane, long-term central venous catheter. Its accessories include a protective adapter female cap, introducer needle, needle obturator, double-ended J guidewire, scalpel, catheter clamp, rigid fastener, a vessel dilator, and silver Dacron® cuff.

The provided 510(k) summary for the Seldiflex® central venous catheter describes a device that is exempt from a clinical study to prove acceptance criteria because it focuses on demonstrating substantial equivalence to predicate devices through functional and safety testing, rather than an AI/ML-driven device requiring performance metrics. Therefore, many of the requested categories related to AI/ML device performance studies are not applicable to this submission.

Here's the information that can be extracted or deduced from the provided document:

Acceptance Criteria and Device Performance (Not Applicable for AI/ML performance)

This submission focuses on demonstrating substantial equivalence to predicate devices based on functional design, materials, and indications for use, rather than meeting specific quantifiable performance metrics typically associated with AI/ML devices. The "acceptance criteria" are implied by successful functional and safety testing and similarity to predicate devices.

Study Information (Relevant to the provided document)

Since this is for a medical device (central venous catheter) that is not an AI/ML device, many of the requested study details (sample size for test set, data provenance, ground truth experts, adjudication, MRMC study, standalone performance, training set details) are not applicable in the context of this 510(k) submission.

1. Sample size used for the test set and the data provenance: Not specified. The document states "Functional and safety testing... consisted of examination of the function of the device under conditions similar to those found in normal usage and testing to ensure conformance to product specifications." This implies in-house testing, likely with a sample size determined by internal quality standards for mechanical and material properties, rather than a clinical trial. Data provenance is not described.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. For a central venous catheter, "ground truth" would relate to its physical properties, biocompatibility, and functional integrity, which are assessed through engineering and material science testing, not typically by clinical experts establishing ground truth in the way it's done for diagnostic AI/ML.

3. Adjudication method for the test set: Not applicable. The testing described (functional and safety) would involve objective measurements and observations against pre-defined specifications without the need for an adjudication process.

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 not an AI/ML device, so no MRMC study was conducted.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an AI/ML device.

6. The type of ground truth used: For this type of device, the "ground truth" for the functional and safety testing would be based on engineering specifications, material standards, and established performance benchmarks for similar medical devices. For example, testing for tensile strength (material property), flow rates (functional property), or biocompatibility (safety).

7. The sample size for the training set: Not applicable. There is no AI/ML component, therefore no training set.

8. How the ground truth for the training set was established: Not applicable. There is no AI/ML component or training set.

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