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The SixFix® Hexapod Fixator is intended to be used for post-traumatic joint contracture which has resulted in loss of range of motion; fractures and disease which generally may result in joint contractures or loss of range of motion and fractures requiring distraction; open and closed fracture fixation; pseudo-arthrosis of long bones; limb lengthening by epiphyseal or metaphyseal distraction of bony or soft tissue deformities; correction of bony or soft tissue defects; joint arthrodesis; infected fractures or nonunions.

The SixFix Hexapod Fixator is a multilateral circular external fixation system. The system includes the following external fixator elements: rings, arches, struts, threaded rods, reduction struts, and assembly accessories. All the elements are provided non-sterile and are for single use only.

This document is a 510(k) summary for "Accessories for the SixFix Hexapod Fixator". It is a premarket notification for a Class II medical device, and therefore a clinical study to prove the device meets acceptance criteria is not typically required. This 510(k) summary describes a performance data study, specifically a mechanical testing study, rather than a clinical study involving human patients or a complex algorithm.

Here's a breakdown of the information requested, based on the provided text:

1. A table of acceptance criteria and the 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: Not explicitly stated in terms of a number of devices or tests. The document refers to "The SixFix Reduction Struts" in the plural, implying multiple units were tested, but a specific quantity is not given.
• Data Provenance: Not explicitly stated. This was mechanical testing, likely conducted by the manufacturer or a contracted testing facility.

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 was a mechanical engineering study, not a clinical study requiring expert interpretation of medical data. The "ground truth" would be the measured physical properties of the tested components.

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

• Not Applicable. As a mechanical engineering study, there is no "adjudication method" in the clinical sense. The results are direct measurements.

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 mechanical external fixator accessory, not an AI-assisted diagnostic or treatment device.

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

• Not Applicable. This device is a mechanical external fixator accessory, not an algorithm.

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

• Mechanical properties/measurements: The ground truth for this mechanical performance study would be the measured forces, displacements, and stiffness values obtained during the ASTM F1541 static axial compression testing.

8. The sample size for the training set

• Not Applicable. This is not a machine learning device; therefore, 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 a machine learning model, this question is irrelevant to this device submission.

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The SixFix™ Hexapod and Deformity Analysis and Correction Software (DACS) are intended to be used for posttraumatic joint contracture which has resulted in loss of range of motion; fractures and disease which generally may result in joint contractures or loss of range of motion and fractures requiring distraction; open and closed fracture fixation; pseudo-arthrosis of long bones; limb lengthening by epiphyseal or metaphyseal distraction; correction of bony or soft tissue deformities; correction of bony or soft tissue defects; joint arthrodesis; infected fractures or nonunions.

The SixFix™ Hexapod Fixator is a multilateral circular external fixation system. The system includes the following external fixator elements: rings, footplates, arches, struts, threaded rods, wires, external fixation accessories, and software. All of the elements are provided non-sterile and are for single use only.

The system is designed such that gradually adjusting the lengths of the struts in relation to one another alters the orientation of the rings and, consequently, the bone segments connected to the rings by half-pins and wires during the treatment period in order to achieve the patient's treatment goals.

The Deformity Analysis and Correction Software is an optional software component and is used to assist the physician in calculating the lengths of the struts connecting the rings to manipulate the bone fragments. The software receives inputs from the physician and allows the physician to visualize the moving bone position. The program computes the strut lengths necessary to implement any desired translation and/or rotation required by the surgeon.

The provided text describes the SixFix™ Hexapod Fixator and Deformity Analysis and Correction Software (DACS) but does not detail specific acceptance criteria or a study that rigorously proves the device meets those criteria with quantitative performance metrics.

Instead, the document highlights:

• Substantial Equivalence: The primary assertion is that the SixFix™ Hexapod Fixator and DACS are "substantially equivalent" to predicate devices (Taylor Spatial Frame, Deformity Analysis and Correction Software (DASC), and Ilizarov External Fixation Wires). This means it performs as well as or better than legally marketed predicate devices without raising new questions of safety or effectiveness.
• Performance Data Overview: It mentions that "Static and dynamic mechanical testing were performed" and "Software verification and validation testing were conducted." However, it does not provide the results of these tests in terms of specific acceptance criteria.

Therefore, an answer using the requested format would mostly consist of "Not Provided" or "N/A" for many fields, as the document does not contain the level of detail asked for in the prompt regarding a specific study proving quantifiable acceptance criteria.

Here's an attempt to answer based on the given text, with the understanding that much information is missing:

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

2. Sample sized used for the test set and the data provenance
Not provided in the document. The text refers to "static and dynamic mechanical testing" and "software verification and validation testing" but does not specify sample sizes for these tests or the provenance of any data used (e.g., country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable/Not provided. The document focuses on substantial equivalence based on mechanical and software testing, not on clinical performance evaluated against expert ground truth for interpretation or diagnosis.

4. Adjudication method for the test set
Not applicable/Not provided. No clinical test set or expert adjudication process is described in the provided text.

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/Not provided. This document does not describe an MRMC study. The DACS is software to assist the physician in calculating strut lengths and visualizing bone position, not for diagnostic interpretation by human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
While the DACS is a standalone software component in terms of its execution, the performance demonstration focuses on its functional equivalence to a predicate software and its role in assisting a physician. No specific "standalone performance" study (e.g., accuracy of calculations compared to a gold standard without human input as part of the evaluation) is detailed with quantifiable results. The text states, "The DACS matches the functions performed by the predicate DACS."

7. The type of ground truth used
For the mechanical testing, the ground truth would likely be established engineering standards or validated predicate device performance data.
For the software testing, the ground truth would be the expected output or behavior according to the software requirements specification and comparison to the predicate software's known functionality.
Specific details are not provided.

8. The sample size for the training set
Not applicable/Not provided. The document describes software verification and validation, not a machine learning model that would require a "training set" in the conventional sense.

9. How the ground truth for the training set was established
Not applicable/Not provided. As no training set for a machine learning model is mentioned, this information is not relevant to the provided text.

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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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