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The intended use of the distal radius fracture fixation plate system is internal fixation of fractures and osteotomies of the distal radius. This may include:

• Displaced or non-displaced fracture which may or may not involve angulation or . fragmentation of bone,
• Failed fracture fixation with or without bone graft, ●
• Osteotomy and repair of distal radius malunion with or without bone graft. .

The distal radius plate like the predicate device includes various size plates, right, left, small, large, accessories and instruments. The bone screws enable the plate to be coupled to bone by securing the screws for the intended use. The various components within the system are provided to accommodate various anatomies and injuries.

The provided text is a 510(k) summary for a medical device (Distal Radius Fracture Fixation Plate System) and does not describe primary studies with acceptance criteria or performance metrics for a novel diagnostic or AI-driven device. Instead, it focuses on demonstrating substantial equivalence to predicate devices based on design and intended use.

Therefore, most of the requested information (acceptance criteria table, sample sizes, expert qualifications, adjudication, MRMC studies, standalone performance, ground truth types and establishment methods) cannot be extracted from this document as it pertains to a different type of regulatory submission and device evaluation.

However, I can provide the following based on the available information:

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

This document does not present acceptance criteria or reported device performance in the manner requested for a diagnostic or AI device. The "performance" being assessed here is the substantial equivalence of the new device's design, materials, and intended use to existing predicate devices.

Study Proving Device Meets Acceptance Criteria:
The study is a comparison of the new device to predicate devices based on design characteristics and intended use to demonstrate "substantial equivalence" as required for a 510(k) application. The document explicitly states: "The device and the predicate device have similar design characteristics and intended use. The new device is substantially equivalent to the predicate device."

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. This is a comparison of device specifications, not a clinical trial or performance study involving a test set of 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. Ground truth as typically understood for diagnostic/AI devices is not relevant to this type of device comparison. The "truth" being established is the similarity in design and function to legally marketed predicate devices, which is assessed by the FDA based on the provided technical documentation.

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

Not applicable. There is no test set or adjudication process in the context of a performance study described here.

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-assisted diagnostic device, and therefore no MRMC study addressing human reader improvement with AI would have been conducted or reported here.

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

Not applicable. This is not an algorithm or AI device.

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

Not applicable in the sense of clinical ground truth for diagnostic accuracy. The "ground truth" for this 510(k) submission is the established design, material, and intended use of the predicate devices, against which the new device is compared for substantial equivalence.

8. The sample size for the training set

Not applicable. There is no training set for an AI algorithm described.

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

Not applicable. There is no training set for an AI algorithm described.

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