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The AIR-BAND™ Radial is a compression device to assist hemostasis of the radial artery after a transradial procedure.

AIR-BAND™ is a sterile, single use, 6-month shelf life, disposable device. It has a clear medical grade polyurethane window and bulb that facilitates visualization of the puncture site, a clear medical grade PVC flexible tube, and a pressure sensitive, self-adhesive peel backing. A luer valve on the end of the fill tube enables a luer lock syringe to be connected to inflate and deflate the bulb with air to provide compression of the transradial puncture site.

The provided text describes the Datascope Corp.'s AIR-BAND™ Radial Compression Device, including its intended use, design characteristics, and performance data used for its 510(k) submission. However, it does not contain the specific acceptance criteria or a detailed study that proves the device meets those criteria in the format requested.

The document states that "Bench top testing was used to demonstrate substantial performance equivalence to the predicate devices." and lists the types of testing conducted:

• Biocompatibility Testing
• Pressure Equivalence to Predicate (TR Band)
• Packaging Performance
• Product Stability (Shelf Life)
• Product Sterilization

It concludes that "The results of all testing demonstrate that the AIR-BAND™ Radial Compression Device is as safe, as effective, and performs as well as the predicate devices."

Based on the provided information, I can only provide limited details about the acceptance criteria and study as they are not explicitly detailed in the prompt in the format requested.

Here's an attempt to answer the questions based on the available text:

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

The document does not explicitly state numerical acceptance criteria and reported numerical performance values in a table format. It generally states that "Bench top testing was used to demonstrate substantial performance equivalence to the predicate devices" for the listed tests.

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

The document mentions "Bench top testing," but does not specify the sample sizes for these tests. It also does not provide information about the country of origin or whether the data was retrospective or prospective.

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)

This information is not provided in the document. The testing described is benchtop, not clinical, so "ground truth" in the context of expert review for clinical performance is not applicable here.

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

This information is not provided in the document. As the testing was benchtop, a clinical adjudication method would not be relevant.

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

No MRMC study was mentioned. The device is a physical medical device (radial compression device), not an AI/software product, so this type of study is not applicable.

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

This question is not applicable as the device is a physical medical device, not an algorithm.

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

For the benchtop tests, the "ground truth" would be established by validated test methods and specifications relevant to each test (e.g., biocompatibility standards, pressure measurement standards, packaging integrity standards, sterilization standards). No expert consensus, pathology, or outcomes data are mentioned as ground truth.

8. The sample size for the training set

This information is not provided and is not applicable, as the device is not an AI/machine learning model that requires a training set.

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

This information is not provided and is not applicable, as the device is not an AI/machine learning model.

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