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The GORE SEAMGUARD® Bioabsorbable Staple Line Reinforcement Material is indicated for use in surgical procedures in which soft tissue transection or resection with staple line reinforcement is needed. GORE SEAMGUARD® Bioabsorbable Staple Line Reinforcement Material can be used for reinforcement of staple lines during hysterectomy, lung resection, liver resection, bladder reconstruction, bronchial, bariatric, colon, colorectal, esophagus, gastric, mesentery, pancreas, small bowel, and spleen procedures. GORE SEAMGUARD® Bioabsorbable Staple Line Reinforcement Material is also intended to be used for reinforcement of suture-lines and staple-lines (i.e., occlusion of the left atrial appendage during open chest procedures) during cardiac surgery.

The GORE SEAMGUARD® Bioabsorbable Staple Line Reinforcement Material for open staplers consists of a bioabsorbable membrane comprosed of a microporous structure of synthetic bioabsorbable glycolide and trimethylenc carbonate copolymer. The GORE SEAMGUARD® Bioabsorbable Staple Line Reinforcement Material is to be used with surgical stapling devices.

The GORE SEAMGUARD® Bioabsorbable Staple Line Reinforcement Material is supplied in sterile polyethylene terphthalate / polyethylene pouches. These pouches contain the necessary material for the cartridge jaw and the anvil jaw of the surgical stapler.

This is a 510(k) premarket notification for a medical device (Staple Line Reinforcement Material), not an AI/ML device. Therefore, the requested information regarding acceptance criteria, study details, sample sizes, expert involvement, and ground truth establishment, which are typical for studies validating AI/ML performance, are not present in this document.

The document states that the device is "substantially equivalent" to a predicate device based on "A variety of tests, assessments, and comparisons" in terms of "composition, design, intended use, mode of operation and performance attributes." However, it does not provide specific details about these tests, the acceptance criteria, or the reported performance outcomes.

Therefore, I cannot provide a table of acceptance criteria and reported device performance or address the other points related to AI/ML device validation.

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