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The Prelude Guide Sheath Introducer is indicated to be used for the introduction of interventional and diagnostic devices into the peripheral (and coronary) vasculature.

The Prelude Guide Sheath Introducer is a sterile, disposable device consisting of a (a) a coil reinforced shaft with an atraumatic tip and the distal end: (b) a hemostasis valve with a side port and color-coded stopcock; and (c) a tapered tip dilator with snap-fit hub at the proximal end.

This is a 510(k) summary for a medical device (Prelude Guide Sheath). The document focuses on demonstrating substantial equivalence to a predicate device based on non-clinical performance data and material comparisons, not on evaluating an AI/ML algorithm. Therefore, many of the requested criteria related to AI/ML device studies (such as sample size for test/training sets, expert ground truth, MRMC studies, standalone performance, etc.) are not applicable or readily extractable from this specific document.

However, I can provide the acceptance criteria and performance as described for the non-clinical tests.

Acceptance Criteria and Reported Device Performance

The document states that "The results of the testing demonstrated that the subject Prelude Guide Sheath met the predetermined acceptance criteria applicable to the performance of the device." The specific numerical acceptance criteria for each test are not provided in this summary, but the general categories of tests performed are listed. The "Reported Device Performance" for each test is simply that the device met those unstated criteria.

Here's a table based on the provided "Performance Tests" section:

The remaining requested information is largely irrelevant to this 510(k) submission, as it pertains to AI/ML algorithm validation for which this device is not. This submission focuses on the physical and material characteristics of a medical device (a guide sheath introducer) and its substantial equivalence to existing predicate devices, rather than the performance of an AI/ML system.

Therefore, the following information is not applicable or extractable from the provided document:

2. Sample sized used for the test set and the data provenance: Not an AI/ML study, so "test sets" in that context don't apply. The non-clinical tests would have their own sample sizes for a variety of physical or chemical tests, but these are not detailed as a single "test set" for an algorithm.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not an AI/ML study; no "ground truth" in the diagnostic/interpretive sense is established by experts here.
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.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
7. The type of ground truth used (expert concensus, 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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