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The AxSOS® Locked Plating System of the Stryker Locked Plating System is intended for use in long bone fracture fixation. The AxSOS Locking Plates are indicated for fixation of long bone fractures including fractures of the distal radius, the proximal humerus, the distal tibia, proximal tibia and the distal femur.

This Special 510(k) submission is intended to address modifications to the predicate Stryker Locking Inserts. The AxSOS® Locking Insert is being modified as part of a line extension of the AxSOS® Locked Plating System. The AxSOS® Locked Plating System contains 5mm Locking Inserts to which changes are being made to improve manufacturability. The modifications to the 5mm AxSOS® Locking Insert are to remove undefined free-form surfaces and replace them with clearly defined surfaces that mate with the plate hole geometry. The removal of these undefined free-form surfaces improves manufacturability, inspectability, and allows for a more precise fit into the plate holes. The changes are identical to the changes to the 4mm Locking Inserts cleared in K092419.

The provided document describes a 510(k) submission for a medical device called the "AxSOS® Locked Plating System Line Extension of 5mm Locking Inserts." This submission focuses on modifications made to existing locking inserts within the system to improve manufacturability and fit.

The information primarily discusses the mechanical and functional testing performed to demonstrate that the modified device is substantially equivalent to its predicate devices. It does not present acceptance criteria or a study related to human reader performance, algorithms, or ground truth derived from expert consensus, pathology, or outcomes data in the way typically found for diagnostic or AI-driven devices.

Therefore, many of the requested sections about acceptance criteria and study details for an AI/diagnostic device are not applicable or cannot be extracted from this document.

Here's the information that can be extracted or derived based on the provided text:

1. Table of acceptance criteria and the reported device performance

2. Sample sized used for the test set and the data provenance

• Sample Size for Test Set: Not specified. The document mentions "testing was conducted" for various mechanical tests but does not provide specific sample sizes (e.g., number of inserts or constructs tested).
• Data Provenance: Not applicable in the context of clinical data or patient data. The testing is described as mechanical and functional in nature, likely performed in an engineering lab setting.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not applicable. The "ground truth" in this context is based on engineering measurements and established mechanical testing standards, not clinical expert consensus.

4. Adjudication method for the test set

• Not applicable. This type of mechanical testing generally relies on direct measurement and comparison to predefined engineering specifications or predicate performance, rather than expert adjudication.

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. This is a submission for a mechanical implant (bone fixation system), not a diagnostic device or AI system that involves human readers.

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

• No. This is not an algorithmic device. The "standalone" performance here refers to the mechanical performance of the device components themselves.

7. The type of ground truth used

• Ground Truth: The "ground truth" for the performance claims is based on established engineering principles and mechanical testing standards comparing the modified device's performance directly to its predicate device. This includes measurements from tests like Construct Torsion, Insertion Force, Cantilever Bending, Push-Out, and Construct Fatigue.

8. The sample size for the training set

• Not applicable. There is no "training set" in the context of an AI/machine learning model for this device. The development process involved design modifications and subsequent mechanical testing.

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

• Not applicable, as there is no training set mentioned for an AI/ML model.

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