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CaP Spheres Pellet Pack™ is intended for use as a bone void filler for bony voids or gaps that are not intrinsic to the stability of the bony structure. These defects may be surgically created osseous defects resulting from traumatic injury to the bone. CaP Spheres Pellet Pack™ is intended to be used in conjunction with autograft bone as a bone graft extender and gently packed into bony voids or gaps in the posterolateral spine. CaP Spheres Pellet Pack™ provides a bone void filler that resorbs and is replaced by the growth of new bone during the healing process.

CaP Spheres Pellet Pack™ is a synthetic, osteoconductive, and resorbable bone void filler device consisting of hollow and microporous biphasic calcium phosphate spheres contained within a resorbable mesh pouch to aid in intraoperative handling. The device may be implanted directly, or hydrated with saline or autologous blood, and is intended to be combined with autograft.

The provided text describes a medical device, CaP Spheres Pellet Pack™, and its FDA 510(k) submission. However, it does not contain information about acceptance criteria, a study proving the device meets those criteria, or details regarding AI/ML device performance.

The document is a 510(k) clearance letter and summary for a physical medical device (bone void filler), not an AI/ML powered device. Therefore, the requested information regarding acceptance criteria, test set details, expert involvement, MRMC studies, or standalone algorithm performance, as commonly applied to AI/ML devices, is not present in this text.

The text focuses on:

• Device Description: What the CaP Spheres Pellet Pack™ is.
• Indications for Use: When and how the device should be used.
• Substantial Equivalence: Comparison to existing, legally marketed predicate devices to show it's as safe and effective.
• Performance Testing: Bench testing (physical, chemical, degradation), biological safety, sterilization, shelf-life, and an in vivo animal study.

The closest information related to "acceptance criteria" is the overall demonstration of "substantial equivalence" to predicate devices, and that all performance tests "yielded acceptable results." However, specific numerical acceptance criteria for performance metrics (like sensitivity, specificity, AUC) are not defined, as they are not typically applicable to this type of device in the same way they are for diagnostic AI/ML products.

Therefore, I cannot provide a table of acceptance criteria and reported device performance, or answer the subsequent questions, as the input document does not contain this information for an AI/ML device.

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