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The Inset Reverse Total Shoulder System should be used in patients whose shoulder joint has a grossly deficient rotator cuff with severe arthropathy and/or previously failed shoulder joint replacement with a grossly deficient rotator cuff. The patient must be anatomically and structurally suited to receive the implants and a functional deltoid muscle is necessary.

The Inset Reverse Total Shoulder System is indicated for primary or revision total shoulder replacement for the relief of pain and significant disability due to gross rotator cuff deficiency.

The Glenoid Baseplate is intended for cementless application with the addition of screw fixation. The Humeral Stem may be implanted by press-fit or cement fixation.

The Inset Reverse Total Shoulder System is intended for total shoulder replacement in a reverse shoulder configuration. Unlike traditional total shoulder replacement, a reverse shoulder employs a ball for articulation on the glenoid side of the joint and a polyethylene bearing surface on the humeral side of the joint.

For the Inset Reverse Total Shoulder System, a Glenosphere Baseplate is attached to natural bone on the glenoid side of the joint with a Central Compression Screw and Peripheral Screws. The baseplate includes a female taper to mate with the chosen Glenosphere. Glenoid baseplate components are indicated for a press-fit cement-less application with the addition of screw fixation. The chosen Humeral Bearing is attached to the Humeral Tray and the assembly is attached to the Humeral Stem (K173824) to complete the humeral side of the joint. Humeral stems are indicated for press-fit uncemented use or for use with bone cement.

The material used in the manufacture of the humeral stem, glenoid baseplate, modular tray, compression screw, and supplementary screws are titanium alloy according to ASTM F136.

The humeral stem and the glenoid baseplate have a proximal porous coating of commercially pure titanium according to ASTM F67. The glenosphere is available in cobalt-chromium (CoCr) alloy, per ASTM F1537. The humeral bearing is manufactured from ultrahigh molecular weight polyethylene (UHMWPE) according to ASTM F648. The instruments are manufactured from stainless steel and acetal copolymer.

This document is a 510(k) Premarket Notification from the FDA regarding the "Inset Reverse Total Shoulder System". The key takeaway is that this document does not describe any acceptance criteria or studies proving device performance from an AI/algorithm perspective. Instead, it focuses on the substantial equivalence of a physical medical device (shoulder implant) to previously marketed predicate devices.

Therefore, I cannot fulfill the request as it pertains to AI/algorithm performance. The provided text is solely about the regulatory clearance of a physical orthopedic implant.

Here's why the supplied text doesn't contain the requested information about an algorithm's performance:

• Product Type: The device is a "Shoulder joint metal/polymer semi-constrained cemented prosthesis" – a physical implant, not a software algorithm or AI.
• Study Types: The "Non-Clinical Testing" section lists mechanical tests (e.g., fatigue, torque, strength) and a cadaver study, which are typical for orthopedic implants. There is no mention of algorithm performance metrics like sensitivity, specificity, AUC, etc.
• "Clinical Testing" section: Explicitly states, "Clinical testing was not necessary to demonstrate substantial equivalence of the Inset Reverse Total Shoulder System to the predicate device." This reinforces that no human-in-the-loop or standalone AI performance study was conducted.
• Ground Truth: The concept of "ground truth" for an AI system (e.g., expert consensus on image findings, pathology reports) is irrelevant for the mechanical testing of a physical implant.

To answer your request, if this were an AI algorithm, the information would typically be in a section describing the validation study for the software, often detailing metrics like:

• Acceptance Criteria for AI: Often include thresholds for sensitivity, specificity, positive predictive value, negative predictive value, or F1-score.
• Study Design: Details on the dataset (size, origin, retrospective/prospective), reader study design (MRMC, standalone), expert qualifications, and truth establishment.

Since the provided text is about a physical medical device, not an AI, it does not contain the information needed to answer the specific questions about "acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of an AI/algorithm.

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