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The CoLink® NeoFuse Plating System is indicated for anterior fixation of ankle arthrodesis and fractures, including the distal tibia and talus.

The addition of a compression screw through the tibiotalar joint (example CoLag 6.7mm screw) is required.

The In2Bones CoLink® NeoFuse Plating System includes sterile fusion plates and screws for anterior fixation of ankle arthrodesis and fractures, including the distal tibia and talus. The addition of a medial-lateral compression screw through the tibiotalar joint is required. These subject devices are part of the overarching CoLink® Plating System and will be commonly referred to as the CoLink NeoFuse Plating System.

This is not an AI/ML device, so elements of the typical AI/ML acceptance criteria and study information are not applicable. The provided text describes a traditional medical device (metallic bone fixation system) and its regulatory clearance process, which focuses on demonstrating substantial equivalence to previously cleared predicate devices through performance testing and material comparisons.

Here's a breakdown based on the provided text, indicating where information is not applicable (N/A) for this type of device:

Device Name: CoLink® NeoFuse Plating System
510(k) Number: K213069

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size: The document does not specify the exact number of plates or screws tested. It refers to "testing conducted per a modified ASTM F382 set up" for plates. For biocompatibility, sterilization, packaging, and endotoxin tests, these are typically conducted on representative samples of the device and its components, but specific sample numbers are not provided.
• Data Provenance: Not explicitly stated, but typically, these types of performance tests are conducted in a laboratory setting (e.g., by the manufacturer or a contract testing facility) in support of regulatory submissions. This is not clinical data, so terms like "retrospective" or "prospective" do not apply.

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)

• N/A. This is a hardware device review, not an AI/ML diagnostic or prognostic device that relies on ground truth established by experts interpreting clinical data. Ground truth in this context refers to the validated performance of the physical device under various engineering tests. Expert involvement would be in the design, testing methodology, and interpretation of engineering data, rather than clinical interpretation.

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

• N/A. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies where multiple experts evaluate ambiguous cases or results for AI/ML performance evaluation. This device's evaluation is based on objective engineering tests and material comparisons.

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

• N/A. No MRMC comparative effectiveness study was performed as this is not an AI/ML device designed to assist human readers.

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

• N/A. This is not an AI/ML algorithm. Its "performance" is assessed through physical and material testing, not through algorithmic output.

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

• Engineering Standards and Predicate Device Performance: The "ground truth" for this device's acceptance is based on its ability to meet established engineering standards (e.g., ASTM F382, ISO 10993-1, ISO 11137-2, ISO 11607-1, ANSI/AAMI ST72) and demonstrate "substantial equivalence" in mechanical properties, biocompatibility, sterilization, and function to previously cleared predicate devices. The performance of predicate devices against these standards serves as a benchmark.

8. The sample size for the training set

• N/A. This is not an AI/ML device, so there is no "training set."

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

• N/A. As there is no training set for an AI/ML algorithm, this question is not applicable.

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