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The Arrowhead Mini-Rail Fixator is indicated for stabilizing various fractures including open and comminuted fractures, infected non-unions, fractures with length discrepancies, fusions and corrective osteotomies of the metarsal, ulnar, and calcaneal bones.

The Arrowhead Mini-Rail Fixator is a unilateral fixator that provides a stable solution for fractures, for lengthening of bones and for correcting deformities.
The Fixation Clamps are capable of controlled linear translation along the rail and of applying either compression or distraction forces. Because the Fixation Clamps can move along the rail independently of one another, a distraction force can be applied at one location along the Fixation Rail and distraction forces applied at another location along the same Fixation Rail.
The Fixation Clamps are capable of securing Fixation Screws with a diameter of 1.6mm to 3.0mm. The rail system and clamps are manufactured from aluminum and stainless steel. The fixation screws are stainless steel and available with and without hydroxyapatite coating.
The fixation screws are provided sterile while the non-implantable fixator components are provided non-sterile.

The provided text describes a 510(k) premarket notification for a medical device called the "Arrowhead Mini-Rail Fixator." This document is from the FDA and pertains to the device's substantial equivalence to legally marketed predicate devices.

Crucially, the provided document DOES NOT describe a study involving an AI/Machine Learning device or a study that uses radiological images and expert consensus. It describes a mechanical fixation device for bones and its evaluation through Finite Element Analysis (FEA) to demonstrate substantial equivalence to predicate devices, focusing on mechanical performance (strength and stiffness).

Therefore, I cannot provide details on acceptance criteria and study proving device meets acceptance criteria regarding AI/ML performance metrics, sample sizes for test sets, data provenance, number of experts, adjudication methods, MRMC studies, standalone performance, or ground truth establishment relevant to AI/ML.

The only "performance data" mentioned is:

1. Table of Acceptance Criteria and Reported Device Performance (Mechanical, not AI/ML):

2. Sample Size for Test Set and Data Provenance:

• The study used Finite Element Analysis (FEA). This is a computational simulation, not a study on a physical "test set" of patients or images in the way an AI/ML study would. There isn't a "sample size" of patients or images in this context.
• Data provenance is not applicable as it's a computational analysis, not patient data.

3. Number of Experts and Qualifications:

• Not applicable. The evaluation was based on engineering analysis (FEA) against a standard (ASTM F1541-02). There's no mention of human experts interpreting results in the way radiologists would for an imaging AI.

4. Adjudication Method:

• Not applicable. This was an engineering analysis, not a human review process requiring adjudication.

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

• Not applicable. This is not an AI/ML imaging device where human readers would be assisted by AI.

6. Standalone Performance (Algorithm Only):

• Not applicable. There is no algorithm in the sense of an AI/ML model for image interpretation. The device's "performance" is its mechanical properties as determined by FEA.

7. Type of Ground Truth Used:

• The "ground truth" for this device's performance is derived from established engineering principles and standards (ASTM F1541-02) for mechanical strength and stiffness, used in a computational simulation (FEA). It's not expert consensus, pathology, or outcomes data in the medical diagnostic sense.

8. Sample Size for Training Set:

• Not applicable. This is not an AI/ML device that requires a training set.

9. How Ground Truth for Training Set was Established:

• Not applicable. As there's no training set for an AI/ML model.

In summary, the provided document describes a mechanical device evaluated through engineering simulation, not an AI/Machine Learning device that processes and interprets medical images. Therefore, many of the requested details regarding AI/ML model validation are not present in this document.

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