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The MotoCLIP™/HiMAX™ Step Staple Implant System is indicated for hand and foot bone fragment osteotomy fixation and joint arthrodesis.

The MotoCLIP™/HiMAX™ Step Staple Implant System gives the surgeon a means of bone fixation in the management of bone fractures and reconstruction surgery. The MotoCLIP™/HiMAX™ Step Staple Implant System includes implants and the related instruments needed for implantation. The implants are made of nitinol. Prior to implantation, the legs of the implant are held substantially parallel to facilitate insertion into the prepared bone. Once implanted and released from the insertion device, the implant will move towards each other in a converging fashion. This movement creates compression across the adjoining bone members.

This document describes a 510(k) premarket notification for a medical device, the MotoCLIP™/HiMAX™ Step Staple Implant System. It primarily focuses on demonstrating substantial equivalence to previously cleared predicate devices, rather than proving the device meets specific acceptance criteria through a clinical or standalone performance study in the way one would for an AI/ML device.

Therefore, many of the requested sections (acceptance criteria table, sample size for test/training sets, experts, adjudication, MRMC, standalone performance, ground truth establishment) are not applicable in the context of this 510(k) submission, as it relies on different types of evidence.

However, I can extract information related to the device performance testing and the standard for acceptance in this regulatory pathway.

Here's a breakdown based on the provided document:

1. A table of acceptance criteria and the reported device performance:

For a 510(k) submission, the "acceptance criteria" for demonstrating substantial equivalence primarily revolve around meeting or exceeding the performance of the predicate device(s) in relevant testing. The reported "performance" is a demonstration of this equivalence.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not applicable in the context of a clinical test set. The performance testing was based on finite element analysis (FEA), which is a computational simulation method, not a physical test on a "sample size" of devices in the traditional sense or a patient data set. The FEA was performed "utilizing the worst-case sizes" of the device.
• Data Provenance: Not applicable. FEA is a computational modeling technique, not derived from patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

• Not Applicable. This is not an AI/ML device requiring human expert annotation for ground truth. The "ground truth" here is established by engineering principles and the performance of the predicate device as a benchmark.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Not Applicable. There was no human "adjudication" of a test set as this is not a study involving human interpretation of data (e.g., images).

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:

• No. An MRMC study was not done. This device is a mechanical implant, not an AI-assisted diagnostic tool.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Yes, in spirit. The "standalone performance" of this device was assessed through Finite Element Analysis (FEA) to predict its mechanical properties (bending strength, stiffness, pullout strength) without human intervention in the device's function or the analysis itself (though humans design the FEA). This is the "algorithm only" equivalent for a mechanical device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• For this mechanical device, the ground truth for substantial equivalence is primarily the established mechanical performance and safety profile of the predicate device(s). The FEA results are compared to these established benchmarks to demonstrate that the subject device performs equivalently.

8. The sample size for the training set:

• Not Applicable. There is no "training set" in the context of a 510(k) submission for a mechanical device like this. This is not an AI/ML product that learns from data.

9. How the ground truth for the training set was established:

• Not Applicable. As there is no training set.

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