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The Emerge Medical Distal Radius Set is intended for fixation of complex intra-articular and extra-articular fractures and osteotomies of the distal radius and other small bones.

The Emerge Medical Distal Radius Set will include 6 and 9 locking hole head variations with pairs of locking and non-locking holes in the shafts to be used with a variety and screws to be FDA cleared and offered as a system of implants to be used for internal bone alignment and fixation of fractures of the radius. The system features plates with six and nine hole head variations with three and five hole shafts, bone screws for fixation, and a set of instruments to facilitate installation and removal of the implants. The plates have screw holes, which allow for attachment to the bones or bone fragments. The plates are fabricated from medical grade stainless steel (ASTM F139-12).

The provided document describes a 510(k) premarket notification for the "Emerge Medical Distal Radius Set," a device intended for bone fixation. However, the document does not contain the detailed information necessary to answer the questions about acceptance criteria and a study proving their fulfillment.

This type of submission (510(k)) typically focuses on demonstrating substantial equivalence to a predicate device, rather than proving performance against specific acceptance criteria through extensive clinical studies like those for novel devices or PMAs. The performance data presented is limited to non-clinical (FEA) testing.

Therefore, most of the requested information cannot be extracted from this specific document. Below is an attempt to answer what can be inferred from the provided text, and explicitly state what is missing.

Acceptance Criteria and Device Performance Study for the Emerge Medical Distal Radius Set

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information:

• Specific numerical acceptance criteria for static and dynamic bending (e.g., minimum load capacity, maximum deformation) are not provided in the document.
• The exact numerical performance results (e.g., stress values, displacement) of the FEA for the Emerge Medical device or the predicate are not detailed. Only a qualitative "worst-case scenario" comparison and a conclusion of sufficiency are given.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not applicable in the traditional sense of a clinical or human-subject test set. The performance evaluation was conducted using Finite Element Analysis (FEA), which is a computational method. It likely involved a digital model of the device.
• Data Provenance: Not applicable. FEA is a simulation method.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Not applicable. This study involved non-clinical FEA and did not rely on expert ground truth establishment for a test set. Design and engineering expertise would have been involved in setting up and interpreting the FEA.

4. Adjudication Method for the Test Set

• Not applicable. There was no human-reviewed test set requiring adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No. No MRMC comparative effectiveness study was conducted or mentioned. This device is a bone fixation appliance, not an imaging or diagnostic AI/ML device that would typically undergo such a study.

6. Standalone Performance Study (Algorithm only without human-in-the-loop performance)

• Yes, in spirit, but not an "algorithm" as typically conceived for AI/ML. The "standalone" performance here refers to the mechanical performance of the device itself, evaluated through FEA, without human interaction during the test. The FEA model computationally determined the device's mechanical characteristics.

7. Type of Ground Truth Used

• Engineering Principles and Predicate Device Data. For the FEA, the "ground truth" for comparison and validation would typically involve:
  • Established biomechanical engineering principles.
  • Material properties of medical-grade stainless steel (ASTM F139-12).
  • Potentially, existing mechanical performance data or design specifications of the predicate devices for comparative analysis, although this is not explicitly detailed. The statement that the predicate was the "worst-case scenario" implies a comparison to known performance characteristics, either simulated or from prior testing.

8. Sample Size for the Training Set

• Not applicable. The performance evaluation was a non-clinical FEA, not an AI/ML algorithm that requires a training set. The FEA model itself is "designed" based on engineering specifications rather than "trained" on data.

9. How the Ground Truth for the Training Set Was Established

• Not applicable. There was no "training set" in the context of AI/ML. The FEA model's foundational data (material properties, geometry, boundary conditions) are established through engineering design, material science data, and biomechanical specifications.

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