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This device is intended for use in total knee arthroplasty procedures for the following conditions:

1. Loss of joint configuration and joint function.
2. Osteoarthritis of the knee joint.
3. Rheumatoid arthritis of the knee joint.
4. Post-traumatic arthritis of the knee joint.
5. Valgus, varus, or flexion deformities of the knee joint.
6. Revision procedures where other treatments or devices have failed.

The Offset Junction Box is part of the Balanced Knee® Revision System and is a single use modular device used in revision knee surgery to provide an attachment point between a modular femoral component and a stem extension.
The Offset Junction Box is available in four size options: 0mm and 5mm offset configurations with either a 5° or 7° valgus angle.

This document describes a medical device, the "Balanced Knee Revision System - Offset Junction Box," and its FDA 510(k) clearance. This is a traditional medical device approval, not related to AI/ML software. Therefore, the questions regarding acceptance criteria and studies that prove the device meets these criteria in the context of an AI/ML device cannot be fully answered from the provided text.

The provided text details the device's indications for use, its classification, and the basis for its substantial equivalence to a predicate device (K060569). It mentions mechanical tests conducted but does not provide specific acceptance criteria or detailed results in a comparative table format as requested for AI/ML devices.

However, I can extract the information provided about the non-clinical mechanical tests and state that, based on the document, these tests were used to demonstrate the device's safety and effectiveness in line with established standards for knee joint prostheses.

Here's an attempt to structure the available information, noting the absence of AI/ML-specific details:

Regarding the specific questions for AI/ML devices:

1. A table of acceptance criteria and the reported device performance: As seen above, specific quantitative acceptance criteria or performance metrics (like sensitivity, specificity, AUC) for an AI/ML algorithm are not provided because this is a physical medical device. The "performance" is described in terms of meeting mechanical and biological safety standards.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable for this physical device. The testing involved mechanical tests of the device itself, not analysis of a data set.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI/ML model is not relevant here. The ground truth for device performance would be established by physical testing standards and expert engineering evaluation, but the specifics are not detailed.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable for a physical device.

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: Not applicable. This is not an AI/ML device involving human readers or interpretation.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is a physical device, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For the mechanical tests, the ground truth would be defined by the ASTM standards (ASTM F-1814 for static torsion, static axial and shear, cyclic fatigue) and regulatory requirements for bacterial endotoxin testing. These standards define acceptable performance limits.

8. The sample size for the training set: Not applicable. There is no training set for a physical device.

9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document is a 510(k) clearance letter for a physical knee implant component. It addresses the device's substantial equivalence through mechanical testing against established standards, not through AI/ML model performance evaluation. Therefore, most of the questions tailored for AI/ML devices cannot be answered from this specific text.

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