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Jobst Ready-To-Use Arm Sleeve may be used under the direction of a healthcare professional to manage the flowing conditions: Edema Mild to moderate lymphedema Phlebitis Post-thrombotic syndrome Vascular Malformations Hypertrophic scars

This submission covers JOBST Ready-To-Wear Compression Arm Sleeves, which fall under the device classification of medical support stockings (21 CFR §880.5780). They are equivalent to the preamendment Jobst-Custom garments, and the SIGVARIS 902/503 Arm Sleeves, which are used for the same indications. While Jobst-Custom garments are cut and sewn from fabric made of spandex and nylon varns, the SIGVARIS 902 and JOBST Ready-To-Wear Arm Sleeves are circular knit with spandex and nylon yarns. The sizing of the Ready-To-Wear Arm Sleeves is based on circumferential measurements of the wrist, forearm and mid-upper arm and is limited to people whose arm dimensions fall within the specified ranges. The Jobst-Custom garments are sized based on measurements taken every inch and a half. They can be made to fit a wider range of limb dimensions and a wider range of compression as needed by the individual patient. Compression is provided for all of these products by large elastic yarns that act circumferentially on the limb. The gradient compression present in these products helps to force fluid into the deep venous system and helps in the return of lymphatic fluid.

The provided text is a 510(k) summary for a medical device (compression arm sleeves) and an FDA clearance letter. It does not contain information about acceptance criteria, device performance studies, sample sizes, expert involvement, or ground truth establishment for an AI/ML device.

This document pertains to a traditional medical device (compression garments) that gained market clearance through the "substantial equivalence" pathway, meaning it was deemed as safe and effective as existing legally marketed predicate devices. This type of submission relies on comparisons to established devices and typically does not involve the rigorous testing and validation studies outlined in your request, which are more applicable to novel diagnostic or AI/ML-driven devices.

Therefore, I cannot extract the requested information from the provided text.

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