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The range of indications includes treatment of acute fractures and reconstructive surgery of all long bones.

The Berlin External Fixator is a wire connected ring or frame fixator. It can be used as a static fixator or dynamically applying distraction or compression. The Berlin Fixator has been designed as a modular construction system. Through the use of a few individual components, simple static fixation procedures can be achieved. This devices is made from a combination of aluminum and stainless steel.

Here's an analysis of the provided text regarding the Berlin External Fixator, framed by your requested categories.

This document describes a medical device, the Berlin External Fixator, and its mechanical testing for safety and effectiveness. It is not an AI/ML device, and therefore many of your requested criteria, which pertain to AI/ML software performance evaluations, are not applicable. I will address each point, indicating when the information is not relevant to this type of device submission.

Acceptance Criteria and Study for Berlin External Fixator (K954053)

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

Note: The acceptance criteria for mechanical tests are often implicitly set by demonstrating "substantial equivalence" to a predicate device, meaning the new device performs at least as well or comparably to a legally marketed device. The document implies that achieving similar mechanical properties to the Ilizarov device is the standard for acceptance.

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 test set consisted of three different assemblies of the Berlin Fixator (small, medium, and large diameter rings) and one Ilizarov fixator for comparison.
• Data Provenance: This is a mechanical device, so "data provenance" in the sense of patient data is not applicable. The testing was laboratory-based mechanical testing. The country of origin of the testing facility or data is not specified in this summary. It would be considered prospective testing in a lab setting, as the tests were specifically conducted to evaluate the device.

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 a mechanical device. "Ground truth" in the context of expert review of images or clinical outcomes is not relevant here. The "ground truth" for mechanical testing is derived from controlled laboratory experiments and measurements, not expert consensus.

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

• Not Applicable. As this is mechanical testing, there is no expert adjudication of results in the way there would be for image interpretation. The "results" are objective measurements (e.g., load to failure, number of cycles).

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 device is a physical external fixator, not an AI/ML software. No human reader studies or AI assistance are involved.

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

• Not Applicable. This is a physical medical device, not an algorithm.

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

• Experimental/Objective Measurement: The "ground truth" for this type of device is established through controlled mechanical testing in a laboratory environment, measuring physical properties like static load to failure and fatigue resistance under cyclic loading. The performance is compared directly to a predicate device under identical test conditions.

8. The sample size for the training set

• Not Applicable. This is a mechanical device, not an AI/ML algorithm. There is no "training set."

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

• Not Applicable. As there is no training set for an AI/ML model, this question is not relevant.

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